{
 "cells": [
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   "source": [
    "# Nexus — mapping CORDIS projects to the SDG taxonomy, with the evidence behind every link\n",
    "\n",
    "**What this is.** For 400 CORDIS projects (Horizon Europe, Horizon 2020, FP7), which SDG concepts the project's *own* work contributes to, at the most precise level its text supports, with the project's own sentence that justifies each link, a confidence level, and a record that anyone can re-check without the model. Interactive prototype: **https://nexus.foaap.app**.\n",
    "\n",
    "**In numbers.** 307 projects have at least one justified SDG, 70 have **no justified mapping** among assessed candidates (pending candidates may remain), 915 supported links in total, 46% of them at target level. On 20 untouched projects drawn after the method was frozen (batch C, blind reviewer), 97% (33/34; 95% CI 85–100%) of supported links are on a goal the reviewer also found, 79% (27/34; 95% CI 63–90%) on the exact concept, and the method finds 43% (23/54; 95% CI 30–56%) of the goals the reviewer found.\n",
    "\n",
    "**How to read this notebook.** Every section starts with plain text; the code under it produces the table or figure it talks about, from the published files in this repository. Sections follow the evaluation criteria: data (§2), method and one project followed end to end (§3), taxonomy use (§4), results (§5), validation (§6), limitations (§7), reproducibility and reuse (§8)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132b67c5",
   "metadata": {},
   "source": [
    "## Running the portal export\n",
    "Download the notebook, both CSV files, all `nexus-projects-*.ndjson` parts, `nexus-map-metadata.json`, `sdg-rubrics-2.4.8.json`, `trace-verification.json` and `review-f-score.json` into **one folder**. Open this notebook from that folder and run all cells with Python, pandas, matplotlib and Jupyter. No network access or model calls are required. The NDJSON parts reconstruct the unchanged published project data; only the packaging and input paths differ from the repository notebook. The validation table explicitly identifies A/B as development and C as the untouched holdout.\n",
    "\n",
    "The notebook automatically accepts portal/download suffixes such as `_0` and ` (1)` and verifies each input against an embedded SHA-256. No manual file renaming is needed.\n"
   ]
  },
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    "execution": {
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     "name": "stdout",
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     "text": [
      "Publication snapshot:a87006a46b89c6f0fa3b2…   projects: 400   links in the mapping table: 1069\n",
      "no_mapping               3099\n",
      "supported                 915\n",
      "uncertain                  83\n",
      "insufficient_evidence      71\n",
      "processing_error           77\n",
      "not_assessed             1475\n"
     ]
    }
   ],
   "source": [
    "import json, pathlib, collections\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "ROOT = pathlib.Path.cwd()\n",
    "\n",
    "# Identify the exact attached input bytes even if the portal adds a filename suffix.\n",
    "import hashlib, re\n",
    "INPUT_SHA256 = {'cordis-sdg-mapping.csv': '9e71936a13cccca373a4838199e0b093024764da8216d08371cb988abd2b2be5', 'cordis-projects-summary.csv': 'cc72ff77640dfd3a27cfc22899bca9b9f03c39a1a0e7bd5b577d4922dfd92145', 'nexus-map-metadata.json': 'afc4d7f38f6e7ae61a3b3fae1a63eb5b1beca22a0e15f34ac4532f85d167823d', 'sdg-rubrics-2.4.8.json': '3ceb44aad6e0ca0951cc3cddbac2120867cd46cf4878919cbaff747c379e54d6', 'trace-verification.json': '42896fb254e9cd08e8ad3ddb03f34a3d89dcbbe9152c5f459cbf53dfbfe6808a', 'review-f-score.json': '0036830b98a1ac3942c1587ab8c78c5efeaa624e6a18634245546b37403d404b', 'nexus-projects-01.ndjson': 'febebe9afc6d7326337a4b5bd1f50da51e235ea24ce0a350bd1076e5fb8e19a5', 'nexus-projects-02.ndjson': '220b3636e47e0611b4bf8f452084134cdea105a7a51a10c23ef8253135156136', 'nexus-projects-03.ndjson': '8ffb38bd5d66adccdda2d2d094c03528b4a261db52885ca5399999ac747cb1a3', 'nexus-projects-04.ndjson': '750ef0500d74dfc41794215403fe6e2688edeb7487660b2bbe129ca03bfc42fb', 'nexus-projects-05.ndjson': '5ac54a045a720e208c3ab1ed8e164b1fc218578d84bb6bb2bbc0ecdaaf18fd16', 'nexus-projects-06.ndjson': '9beff30ba1216e7859636a38e94dbe178caa1e7747fdc63f708a7b29f3a73367', 'nexus-projects-07.ndjson': 'ca6466d39a7d99e172174afe65eb8e758ca85499ac688342ba24f3a6ac2ca157', 'nexus-projects-08.ndjson': '10559e3bdf2d209f897b55479f3f1742460497cb55cea05e29d549a0441ff136', 'nexus-projects-09.ndjson': 'bc8a54d98540544881151d2752720e2e5688256d22ae5979a5b00bb172fbd539'}\n",
    "\n",
    "def input_file(name):\n",
    "    expected = INPUT_SHA256[name]\n",
    "    original = pathlib.Path(name)\n",
    "    pattern = re.compile(re.escape(original.stem) + r\"(?:_\\d+| \\(\\d+\\))*\" + re.escape(original.suffix))\n",
    "    candidates = [p for p in ROOT.iterdir() if p.is_file() and pattern.fullmatch(p.name)]\n",
    "    for candidate in sorted(candidates, key=lambda p: (p.name != name, p.name)):\n",
    "        if hashlib.sha256(candidate.read_bytes()).hexdigest() == expected:\n",
    "            return candidate\n",
    "    raise FileNotFoundError(f\"Missing or changed input: {name}. Download the matching attachment into this folder.\")\n",
    "\n",
    "SUB = ROOT; FIG = SUB / \"figures\"; FIG.mkdir(exist_ok=True)\n",
    "data = json.load(open(input_file(\"nexus-map-metadata.json\")))\n",
    "parts = [input_file(f\"nexus-projects-{i:02}.ndjson\") for i in range(1, 10)]\n",
    "if not parts: raise FileNotFoundError(\"Download all nexus-projects-*.ndjson attachments into this folder.\")\n",
    "data[\"projects\"] = [json.loads(line) for part in parts for line in part.read_text().splitlines() if line.strip()]\n",
    "snap = data[\"snapshot\"]\n",
    "assert len(data[\"projects\"]) == snap[\"projects\"], \"Missing project data parts\"\n",
    "m = pd.read_csv(input_file(\"cordis-sdg-mapping.csv\"), dtype=str)\n",
    "p = pd.read_csv(input_file(\"cordis-projects-summary.csv\"), dtype={\"project_id\": str})\n",
    "GOALS = [\"No poverty\",\"Zero hunger\",\"Good health\",\"Quality education\",\"Gender equality\",\"Clean water\",\"Clean energy\",\"Decent work\",\"Industry & innovation\",\n",
    "         \"Reduced inequalities\",\"Sustainable cities\",\"Responsible consumption\",\"Climate action\",\"Life below water\",\"Life on land\",\"Peace & institutions\",\"Partnerships\"]\n",
    "plt.rcParams.update({\"figure.dpi\": 110, \"font.size\": 9})\n",
    "print(f\"Publication {snap['id'][:30]}…   projects: {snap['projects']}   links in the mapping table: {len(m)}\")\n",
    "print(pd.Series(snap[\"states\"], name=\"candidate pairs by state\").to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbc1aec",
   "metadata": {},
   "source": [
    "## 1. The problem\n",
    "\n",
    "Mapping research to the SDGs is usually done by matching words: a project that mentions *climate* is tagged SDG 13. That is fast, and it is often wrong. A project can mention a topic as background, describe earlier work by a partner, or promise a later phase that depends on its own success. The organisers ask for assignments that are *well justified and not based on superficial keyword matching*.\n",
    "\n",
    "Nexus separates two questions a keyword cannot tell apart: **what does this project itself do?** and **does that work contribute to this SDG concept?** Every link must answer both from the project's own text, and every answer is recorded so it can be checked."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba45eeb",
   "metadata": {},
   "source": [
    "## 2. Data\n",
    "\n",
    "* **Source.** The official CORDIS project datasets published on the EU Open Data Portal (one archive per framework programme). Each archive's download URL, catalogue date, retrieval time, size and SHA-256 are recorded, so anyone can check they hold the same bytes (table below; organisers' Q&A 7).\n",
    "* **Collection.** A programme-stratified, seeded draw of 400 projects from a 5,000-project selection. Every figure describes this collection, not all of CORDIS.\n",
    "* **Evidence field.** The project **objective** is the only field any link may cite. It is split into numbered passages with character offsets and a hash of the text, so every quoted sentence can be traced back byte for byte. EuroSciVoc categories and keywords are used only to decide *which* concepts to check, never to assign one.\n",
    "* **Project context.** Funding, call, topic, EuroSciVoc, keywords and participating organisations come from the same official files and are shown in the prototype and the tables; they do not assign SDGs.\n",
    "* **Missing or inconsistent data.** Candidate pairs that were never assessed or could not be processed are kept with their cause and never counted as \"no\". Organisation data is used at organisation level only; no personal fields are processed (Q&A 13).\n",
    "* **Taxonomy.** EU Vocabularies SDG scheme (`http://data.europa.eu/sdg`): 17 goals and 169 targets in scope."
   ]
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>programme</th>\n",
       "      <th>catalogueModified</th>\n",
       "      <th>retrievedUtc</th>\n",
       "      <th>bytes</th>\n",
       "      <th>sha256</th>\n",
       "      <th>downloadUrl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HORIZON</td>\n",
       "      <td>2026-08-07T00:00:00Z</td>\n",
       "      <td>2026-09-24T14:24:19.556231+00:00</td>\n",
       "      <td>36672015</td>\n",
       "      <td>f91d5b6d7f952a4e…</td>\n",
       "      <td>https://cordis.europa.eu/data/cordis-HORIZONpr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H2020</td>\n",
       "      <td>2026-08-07T00:00:00</td>\n",
       "      <td>2026-09-24T14:24:17.545179+00:00</td>\n",
       "      <td>55235626</td>\n",
       "      <td>a2827fe37d220659…</td>\n",
       "      <td>https://cordis.europa.eu/data/cordis-h2020proj...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FP7</td>\n",
       "      <td>2024-12-05</td>\n",
       "      <td>2026-09-24T14:24:15.913410+00:00</td>\n",
       "      <td>32953397</td>\n",
       "      <td>886ee47973584971…</td>\n",
       "      <td>https://cordis.europa.eu/data/cordis-fp7projec...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  programme     catalogueModified                      retrievedUtc     bytes  \\\n",
       "0   HORIZON  2026-08-07T00:00:00Z  2026-09-24T14:24:19.556231+00:00  36672015   \n",
       "1     H2020   2026-08-07T00:00:00  2026-09-24T14:24:17.545179+00:00  55235626   \n",
       "2       FP7            2024-12-05  2026-09-24T14:24:15.913410+00:00  32953397   \n",
       "\n",
       "              sha256                                        downloadUrl  \n",
       "0  f91d5b6d7f952a4e…  https://cordis.europa.eu/data/cordis-HORIZONpr...  \n",
       "1  a2827fe37d220659…  https://cordis.europa.eu/data/cordis-h2020proj...  \n",
       "2  886ee47973584971…  https://cordis.europa.eu/data/cordis-fp7projec...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective length (characters): median 1962, shortest 92, longest 2756; projects without an objective: 0\n"
     ]
    }
   ],
   "source": [
    "src = pd.DataFrame(snap[\"sources\"])[[\"programme\", \"catalogueModified\", \"retrievedUtc\", \"bytes\", \"sha256\", \"downloadUrl\"]]\n",
    "src[\"sha256\"] = src[\"sha256\"].str[:16] + \"…\"\n",
    "display(src)\n",
    "obj = pd.Series([len(x.get(\"objective\") or \"\") for x in data[\"projects\"]])\n",
    "print(f\"Objective length (characters): median {int(obj.median())}, shortest {obj.min()}, longest {obj.max()}; projects without an objective: {(obj == 0).sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "311c0452",
   "metadata": {},
   "source": [
    "## 3. Method — five steps, then one project followed end to end\n",
    "\n",
    "1. **Take.** Official archives, fingerprinted at download; the source text of every project is kept with its hash.\n",
    "2. **Take apart.** A local open-weight model (Qwen3.5-9B, 4-bit, pinned revision, temperature 0) writes an **activity sheet** for each project *before any SDG is shown*: each unit of work with its role (activity, method, output…), whether it is the project's own current work, earlier work, a conditional later phase or background, and the passages that state it. The sheet is frozen and hashed.\n",
    "3. **Rebuild.** Candidate SDG concepts are proposed (embedding similarity to the official SDG texts, plus model screening of all 17 goals). For each candidate the model sees the frozen sheet and **one written rubric**: a *purpose* criterion, a *scope* criterion and a list of *prohibited leaps* (for example: having partners is not a partnership for the goals; doing research is not research-capacity building). It must name the activity and cite passages.\n",
    "4. **Recalculate and correct.** **Code, not the model, decides.** It checks every citation against the frozen text and sheet, can only lower a criterion (background cited as own work → not met; a citation that does not exist or a passage shared with other work → unknown), and derives exactly one state: *supported*, *uncertain*, *insufficient evidence* or *no justified mapping*. When measurement showed a recurring error, the rules were hardened and every earlier supported link on those goals was re-assessed; nothing was overwritten.\n",
    "5. **Publish only what can be proven.** The map is rebuilt from recorded answers and the frozen code of every run; a publication that does not rebuild byte for byte is refused (§8).\n",
    "\n",
    "**Confidence levels** (Q&A 16): *strong* = supported by the project's own activity under an authored rubric, no flag; *weak* = supported under the generic rubric, from a model-nominated candidate or with a review flag (not counted in goal navigation until a person approves it); *uncertain* is the export code for the **open** confidence bucket: uncertain or insufficient evidence. The assessment_state column preserves the exact state; insufficient evidence does not imply a plausible hypothesis.\n",
    "\n",
    "**One project, end to end.** Below, SAFE4ALL (101137814): the passage, the activity the model wrote down before seeing any SDG, the rubric, the two criteria as checked, and the resulting state — for one supported link and one rejected candidate."
   ]
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     "text": [
      "SAFE4ALL × 11 — Make cities and human settlements inclusive, safe, resilient and sustainable\n",
      "  state: supported   claim stage: planned   counts in goal navigation: True\n",
      "  activity written before any SDG was shown (a10, output, current_project, planned): The outcomes aim to manage migration to cities by ensuring communities are better equipped to adapt and cope with climate change impacts while enhancing food security.\n",
      "  the project's own sentence: \"The outcomes aim to manage migration to cities by ensuring that communities are better equipped to adapt and cope with the impacts of climate change while enhancing food security in a rapidly changing world.\"\n",
      "  rubric · purpose: The project explicitly makes cities or human settlements more inclusive, safe, resilient or sustainable through an identified activity or research pathway. A concrete planned contribution is eligible; achieved impact is \n",
      "  rubric · scope: A city, settlement or urban-system setting is substantively part of the intervention or research purpose; no country restriction applies. One substantive goal limb suffices.\n",
      "  checked · purpose: supported — The project explicitly aims to manage migration to cities and enhance food security in a rapidly changing world, directly addressing the need for inclusive and resilient urban environments.\n",
      "  checked · scope: supported — The intervention explicitly targets 'cities' and 'migration to cities' as a core outcome, establishing a substantive urban-system setting for the research purpose.\n",
      "\n",
      "SAFE4ALL × 9 — Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation\n",
      "  state: no_mapping   claim stage: None   counts in goal navigation: False\n",
      "  the project's own sentence: \"SAFE4ALL aims to address the interconnected issues of climate change, food security, ecosystem and disaster management, and migration in Africa by providing user-centred climate services.\"\n",
      "  rubric · purpose: The project explicitly builds or upgrades infrastructure, industrial processes or production capacity, or brings a technology into use in an identified industry or infrastructure through a stated uptake pathway; or it ex\n",
      "  rubric · scope: Identify the infrastructure, industrial process, usable technology, innovation system or research capacity that the project's own work addresses. Basic research can qualify where its stated work has this pathway; a count\n",
      "  checked · purpose: not_met — The project develops climate services and business models, not resilient infrastructure, industrial processes, or production capacity. It does not bring technology into use in an identified industry or increase research \n",
      "  checked · scope: not_met — The project addresses climate services and migration, not the infrastructure, industrial processes, usable technologies, innovation systems, or research capacity defined by SDG 9. The work falls outside the goal's specif\n",
      "\n"
     ]
    }
   ],
   "source": [
    "proj = next(x for x in data[\"projects\"] if x[\"id\"] == \"101137814\")\n",
    "rubrics = {r[\"code\"]: r for r in json.load(open(input_file(\"sdg-rubrics-2.4.8.json\")))[\"concepts\"]}\n",
    "def show(rel):\n",
    "    print(f\"{proj['acronym']} × {rel['code']} — {rel['name']}\")\n",
    "    print(f\"  state: {rel['state']}   claim stage: {rel.get('modality')}   counts in goal navigation: {rel.get('navigationEligible')}\")\n",
    "    for u in rel[\"activityBinding\"][\"selected_units\"][:2]:\n",
    "        print(f\"  activity written before any SDG was shown ({u['ref']}, {u['role']}, {u['ownership']}, {u['modality']}): {u['description']}\")\n",
    "    if rel.get(\"quote\"):\n",
    "        print(f\"  the project's own sentence: \\\"{rel['quote'].split(chr(10))[0][:300]}\\\"\")\n",
    "    r = rubrics.get(rel[\"code\"])\n",
    "    if r:\n",
    "        for c in r[\"criteria\"]:\n",
    "            print(f\"  rubric · {c['id']}: {c['condition'][:220]}\")\n",
    "    for c in rel[\"checks\"]:\n",
    "        print(f\"  checked · {c['id']}: {c['status']} — {c['reason'][:220]}\")\n",
    "    print()\n",
    "show(next(r for r in proj[\"rels\"] if r[\"code\"] == \"11\"))\n",
    "show(next(r for r in proj[\"rels\"] if r[\"code\"] == \"9\" and r[\"state\"] == \"no_mapping\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea86b5b",
   "metadata": {},
   "source": [
    "## 4. Using the taxonomy\n",
    "\n",
    "* **Most precise concept** (Q&A 6, 17): a link is recorded at target level whenever the text supports the target; 46% of supported links are at target level.\n",
    "* **No double counting** (Q&A 20): goal and target assessments are recorded separately and may coexist. Project counts deduplicate each project within a goal; assignment counts include every concept.\n",
    "* **Pathways, not only outcomes** (Q&A 18): a project that plans work towards a target contributes to it; the *claim stage* (planned, reported activity, result, impact) is recorded as the project states it, and never upgraded.\n",
    "* **No justified mapping** (Q&A 19): 70 projects have no supported or open link among assessed candidates; pending candidates may remain. This is a provisional outcome, not a completed negative assessment."
   ]
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     "text": [
      "confidence_level  strong  weak  All\n",
      "concept_level                      \n",
      "goal                 468    22  490\n",
      "target               339    86  425\n",
      "All                  807   108  915\n",
      "\n",
      "Claim stage of supported links:\n",
      "claim_nature\n",
      "planned            911\n",
      "reported_result      4\n",
      "\n",
      "Project outcome:\n",
      "outcome\n",
      "assigned                307\n",
      "no justified mapping     70\n",
      "uncertain only           18\n",
      "not assessed              5\n"
     ]
    }
   ],
   "source": [
    "sup = m[m[\"assessment_state\"] == \"supported\"]\n",
    "print(pd.crosstab(sup[\"concept_level\"], sup[\"confidence_level\"], margins=True).to_string()); print()\n",
    "print(\"Claim stage of supported links:\"); print(sup[\"claim_nature\"].value_counts().to_string()); print()\n",
    "print(\"Project outcome:\"); print(p[\"outcome\"].value_counts().to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c87b61",
   "metadata": {},
   "source": [
    "## 5. Results — which SDGs appear, where, and how certain"
   ]
  },
  {
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     "iopub.execute_input": "2026-10-23T11:42:22.373431Z",
     "iopub.status.busy": "2026-10-23T11:42:22.373346Z",
     "iopub.status.idle": "2026-10-23T11:42:22.504583Z",
     "shell.execute_reply": "2026-10-23T11:42:22.504350Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HYg21v/47PB2cCuVvvfWWreHgVwMP/32k0/O9i8T3MDXfWRERF2jGWkQkTjELyywZJ86kZ3OyS7unDz/80M5MkQ4ejiDNb9PECS5rZplJYt0sban84MRf/0rRI2aE//e//9nCR8xQ0QOXAIvZUf6/aGtV0wvBJWun2T5mkzl5Z0aVtkIEC40aNbKtxDZu3GiDHGbO6LubEgpqEQAxs8ZzJThjNjAycObv8/oQdHI9Ws/eSMyk0xKK944BAYIKZqoJMpm5C59xpsAZwRoz4xS2oigdPcdJc6d4m18IjFl53g8+BwSQzAITxDIwEm2dfEr4fQRhfH5oDUWLKAL/k03JZTtpA8Vad94TBm3YRvoqp+Z3MutJZgCp7bR3IuWdZQgMHFAciwEhZr15v0aMGGEHKPhMkMnA68VMKY87XWgVRfswen8zQ85nzP9+RCJrgu8Rnyc+pyy92Lp1q/3sEkDOmTPnhP52al4bsjl4P0nJZvvYXjIruE6KNm2zwntKp4RsBr+1GQX0KDJG4P7bb7/Z/QmflfDZZ95v1pWznXwfea3C0aebfRafN2o31KlTx+7LyNbg9/E3IjNSREScFOuy5CIiEh1tbWinVKFCBS9Pnjxe9uzZvbJly3oPPPBAopZPtMthl86F9jxnnnmmbdvUpUuXBO2sItG2acCAAV61atW83Llz27/B77rlllu8mTNnpriNJ9JuK7KtUbT2OuHttnyrV6+27ZxoY7Rnzx7bBqhJkyb2NraX9lm0Xdq6dWuqtnXixIneI488YlsC5cqVy7Y4+vPPP6P+P7T84v+JbPmVkpdfftm+jrR9qlWrljdv3jzbjqpZs2YJHrdt2zavQ4cOtsUSz4X3YcKECYnaKj399NOh30ertKlTpyZqk3Qi7bZ69uzpFS9e3MuZM6dXr14925opLe8bRowY4ZUpU8ZuY82aNW3bpsjfmRzac/He8/8XLFjQa9y4sffdd98laFvFZ/Xcc8/1smXLZv+lPZjf1smX1PvFa8VrltLzi3xdsXv3bu+2227z8uXLZ79bXP/xxx+jvg5r1qzxbr311tDns2TJkt51111n37PIdlt8/8Lxu7id1/hEXht8+OGHXp06dexnmv1FpUqVvG7dunnr169P9nWP9nz5zNGmi89ajhw57HPms9mnTx/bhi8S7zfbzd+LhnZxTz31lFexYkX7mvAc+F7wuT548KB9jNptiYjrMvGfWAf3IiIi8YgZP2admVH217afDFJySZcmM2DUqFGndBtFREQk9rTGWkREJAkEwaT+nkhQHW3NKBWWSV8nnVdERESCR2usRUREIlAFnLXOM2bMsOuZTwRrf1lvSq9xCpktXbrUVuBm3W3r1q1P2zaLiIhI7CgVXEREJAJFxvLkyWMrqlPlOal+w9GsW7fOdO3a1aaPM0tNn2iKjlGAisrRIiIiEjwKrEVERERERETSQGusRURERERERNJAgbWIiIiIiIhIGiiwFhEREREREUkDVQWPaJHy008/2V6jWbPqpREREREREcmI/vvvP7Nz505TuXJlkyNHjpT/By/GXn/9da9y5cpe3rx57eXSSy/1Pv/889D9hw4d8jp37uwVLFjQy507t3fDDTd427dvT/A7NmzY4LVo0cLLmTOnV7hwYe/hhx/2/vvvvxPelu+//55Cbrrooosuuuiiiy666KKLLrro4hEjpkbMp2XPPvts24KkXLlyBPnmvffeM9dee61Zvny5qVixounevbuZNm2amThxojnzzDNNly5dTKtWrcycOXPs/3/s2DHbxqRYsWJm/vz5ZuvWreb222+3owpPPPHECW0LM9X4/vvvTfHixU/L8xUREREREZH4RlxZq1atUIzoZLsten4OHTrUXHfddfaJjBs3ztx44432vtWrV9uAm/6gNWvWNF988YVp2bKl+fPPP03RokXtY0aOHGl69+5tduzYYbJly5bqv7t582ZTsmRJs2nTJlOiRInT9vxEREREREQkfp1obBhXxcuYfR4/frw5ePCgufTSS82SJUvMv//+a5o0aRJ6TIUKFUypUqXMggUL7M/8W7Vq1VBQjaZNm5p//vnHBuEiIiIiIiIip1PMU8FBwbA6derY4mF58uQxn376qSlfvrwNrHPmzGny5s2b4PEE0du2bbPX+Tc8qPbv9+9jsXlS9u7day/h0/0iIiIiIiIizgXWBNHLli0ze/bsMZMmTbJrpOfOnXva/+6QIUPMwIEDT/vfERERERERkeCKi8A6e/bspmzZsvZ6jRo17Prpl19+2a6rPnTokNm3b1+CWevt27fbYmXgX2a2w3G/f19yevToYTp27JhogbqIiIiIiGS89kq7du0yR48eNcePH4/15shpliVLFlscO1++fKfk98XVGmsf9dSOHDlig2yKj82cOTN036+//mo2btxoU8fBv1QQp8eYj8fnz5/frsdODi8iC9H9iyqBi4iIiIhkPAcOHDDr1q0zf//9tw2wJfgOHz5sC2CHLw12esa6T58+tjhZ6dKlzf79+20F8G+//dY89thjdgThrrvusi23ChQoYAPhBx54wNSvX99WBAf/L1XC27VrZwYPHmzXVfft29e25TqRiuAiIiIiIpIxEVCjTJkytm2vBN+xY8fsYArLkU/FrHXMA2vSLdq3b2/TsAmkq1SpYqZPn24aN25s76ftVubMmW1aOLPYzZo1MyNGjEgwhT916lTTqVMnO3udO3du06FDBzNgwIAYPisREREREXEFs9QsT1VQnXFkyZLFTsQSYJ8KcdnHOlbUx1pERFy0b861xgV5G0yO9SaIiES1fv16+++5554b602ROHnfne5jLSIiIiIiIuIaBdYiIiIiIiIiaaDAWkREREREJMa+/vprU6lSJbvu97rrrrMFnTNlymQLPCfl1VdfdSp9vUOHDqZVq1aB/JsxL14mIiIiIiKS0XXu3NlcccUV5ssvvzR58uQxuXLlsgWeKc4s8U8z1iIiIiIiIjG2du1a07RpU1soK3/+/LZKebFixeystcQ/BdYiIiIiIiIpOH78uHn66afNeeedZ8444wybgv3SSy/Z+7755htTs2ZNe/s555xjBg4caB/v47HPPvusuf322+1s9Pnnn28+/vjjUGVqgmfaPl1zzTX2+pgxY6Kmgo8ePdpWqmYWu02bNrYHc6RRo0aZ8uXL29ZhpJa//fbbofv8v/XJJ5+Y+vXr21nx2rVrm19++SXB7/j0009NjRo17O8oUqSI3e7wnt933XWXOeuss2y7ZAYDVq9enabXlNeHbbn44ovNtGnT7H28HsWLFzdvvvlmgv+H15pBh927d9ufqdrdunVruy1sE2nfW7ZsMelNgbWIiIiIiEgKnnjiCTN06FAzaNAgG4i+8847pkCBArYtU4sWLUy9evXM8uXLzbBhw+zjXn755QT//4svvmgaNGhgli1bZtdQE6wSHBIok/KN9957z14naI40f/58c88995hu3bqZH3/80VxyySVm8ODBCR7zwQcf2KD+ueeeM6tWrTIDBgwwXbt2NZMnJ2x32K9fP3tZunSpDWgJlH0E9ASq119/vd3WGTNmmCpVqoTuv+mmm8xff/1lb1+8eLEpW7asufLKK82BAwdO+DV95pln7DYzGLBy5Upz3333mRtuuME+P/pM8zqMGzcuwf8zduxY06RJE1OoUCHz77//2sC+YMGCZt68eWb27NmGbtIMUIQPbKQH9bEOoz7WIiLiIvWxFhE5vX2sDx8+bIPo119/PcHsLR577DEbuBIY+mnbBLzMZv/555+h39uoUSPz1ltv2Z8PHTpkZ66ZnW3WrJm9jf93ypQp5uqrrw4FuJdffrnZt2+ffewtt9xiDh48mCBI5rH8XX/7CXIJVgmMfb169TIrVqwwn3/+uX1cmTJl7KCA/zxY001wyjYxQ83f5DH+tob77rvvbNC6bds2O2sMAtgSJUrYAQWC7pQKiTEDP2nSJHPkyBEbEPM8GSTw3Xzzzfb2ESNGmO+//97UqVPHxmnMXh89etSmxw8fPty+Hu+//77NBOA18PH7SaVnIKJWrVoJ/uaJvO/qYy0iIiIiInIKrVmzxgbXBJ2RmBmuW7dugrXQzF6Tjrx3797QbZUrVw5dz5kzpw3+duzYkeptIN2atO1wBJ0+Zox///130759exuI+xdmz7k9XPi2ELDC3xaC8GjPE8zIk35O4Ov//nz58pnt27cn+hupWVPOQAF/K3x7SZH3fxeBMan3H374of35iy++sMH1tddeG9oeXpfw/5/AmzTyE92etFJVcBERERERkdOMNlrhCMRPZbqyvxabNdWsj07ub4f/7A8IpGZb+BvM3n711VeJ7itYsOBJbe/06dNtMByOgQdf27ZtbTo4KfCkgZNGT/q6/zsYbGAGPlLRokVNelJgLSIiIiIikowLLrjABnsUzopMBa9YsaJNz2aFrR+kst6XImbM5p4qFSpUMIsWLUpw28KFCxMEksw+r1u3Luoa7dRiPTXP87bbbkt0X/Xq1e1MPEXaSJNOi0qVKtl0clKt//e//yX5uFtvvdWub2d2mlT58JRutoefee558+Y1saTAWkREREREJBmsPX700UdNz549TdasWc2ll15qi4z98ccftv806dbMqHL9p59+spWuKRx2KnXp0sUWP+NvXXXVVWbq1Klmzpw5du23r3///uahhx6yQSYFvkhfJ/gmNbpTp06p+juPP/64LUZG5XIqbLMWmhnq7t272z7bpGeTik2BNNZ0s4588uTJtgAawX9qsY38Toqr/ffffzad/p9//rHPiVlxf514uXLl7Ay8X1Gd5xUedLOenUJrvN4MZvCeTJw40a41J90+vWiNtYiIiIiISAr69u1rHnzwQdO7d287S82MLq2nCAIpDMYsNbO9BIoE2Tz2VGLd9siRI80LL7xgqlWrZotzPfLIIwkeQ1Xt1157zVbZZh01BdNYn8w65dRq2LChGT9+vJkwYYJ9PgTZrLtG5syZ7Tpn1nazlptAul27dmbnzp0nnAoOgl+Kqz355JP2NaW6OkF86dKlEzyOAJptINhmYMNH2zECcWbqSRHnd9x77712OxkMSU+qCh5GVcFFRMRFqgouInJ6q4JLMK1XVXARERERERGR+KDAWkRERERERNLswgsvTND6Kvwyd+5cE2QqXiYiIiIiIiJp9vnnn5t///036n0UFgsyBdYiIiIiIiKSZqUjio5lJEoFFxEREREREUkDBdYiIiIiIiIiaaDAWkRERERERCQNFFiLiIiIiIiIpIECaxEREREREZE0UGAtIiIiIiIikgZqtyUiIiIiInKanFvv1XT9e+vndTklv6dDhw5m//79ZtKkSafk9wWdZqxFRERERETkpPz777+x3oS4EPPA+plnnjGXXHKJyZs3rylSpIi54YYbzJo1axI8pmHDhiZTpkwJLvfdd1+Cx2zcuNFcddVVJleuXPb3PPLII+bYsWPp/GxERERERETcwYz0RRddZHLkyGHOOuss07RpU9O/f3/zzjvvmI8++igUf3377bdm/fr19vqECRNM/fr1zRlnnGE+/fRTc/z4cfP444+bc845x95Ws2ZNM3v27NDf4P/l//vqq69MtWrVTO7cuc0VV1xh/vzzzwQBepcuXcyZZ55pt2PAgAGmVatWdubcBTEPrHnB77//frNw4UIzc+ZMc+TIEdOkSRNz6NChBI/r1KmT2bp1a+gyePDg0H0E0ATVR48eNfPnz7cfgrffftsMHDgwBs9IREREREQk/hFX3XLLLeauu+4yq1evNl9//bVp3ry5eeihh8xNN91krr766lD8Vbdu3dD/17t3b/sY/p8GDRqYoUOHmpdeeskMGzbMLF++3NSrV8+0aNEiQeAMguURI0bYmG3btm32d/iI7z788EPz/vvvm7lz55oNGzbY+NAVMV9jPX369AQ/jxkzxs44//jjjwnePGaiixUrFvV3fPnll2bVqlVm1qxZpmjRonYUZNCgQfYN79evn8mWLdtpfx4iIiIiIiIuIWD+77//bNZw6dKl7W1VqlSx/+bMmdNOYEaLwXr06GGuvfba0M8vvviieeyxx0zr1q3tzwTYxGYE0U899VTocVz3Y7wHH3zQzoz7XnnlFdOnTx/TsmVL+/Prr79uPv/8c+OKmM9YR9qzZ4/9t2DBggluZxaalADSFHjBw2e0FyxYYKpWrWqDah8pDP/8848dRUnK3r17zebNm0MXPlgiIiIiIiIZATEUy24rV65s2rRpY9566y0bI6WEVO/w+G3r1q12ltpH2jcBNJOf4fg7vuLFi5sdO3aEfsf27dtNrVq1Qvdnz57dbp8r4iqw9jzPdO/e3Vx22WWmQoUKodvbtm1rUwK++eYb06tXLxtkt2/fPnQ/aQThQTX8n7kvKUOGDDElS5YMXcLfSBERERERkSDLkiWLXfc8bdo0U7ZsWfP888+bSpUq2SA3OayRPhnZwjKJCb6J/4Ii5qng4VisvnLlSjNv3rwEt99zzz0JRjnOPvts07hxY5t376csnAxSGDp27Bj6mZEWBdciIiIiIpJRZM6c2RYi40IBMpblzpgxw84Yp6YYNMXGihcvbmM4f9aagJl11Ndff32qtoHfwcTo4sWLQ6ni1M9ivTaxnwviJrB+4IEHzGeffWbmzJmT4otXu3Zt++/atWttYE3e/5IlSxI8xh9lSWpdNvLly2cvIiIiIiIiGc2iRYvsjDXFowsXLmxjMXpXly9f3i6VZZ30b7/9ZpfpEvwmpWfPnrbG1XnnnWcnQllbTQXxzp07n1A8yBrs888/315eeOEFc/jwYTuz7YKYB9aMZvAifvLJJ7YMe5kyZVL8f5YtW2b/ZWQEderUMU8//bTZuXOn/UCACnL58+dPkFIuIiIiIiKSntbP62LiFZOMBNNU9SagJhYbNWqUncgkSCY+q1Gjhr2PZbnnnntu1N/TvXt3uzabgmS7d++2wTWFx05ktpl2yWQQswyY2XKymf/3v//ZNmAuyOTFOLGdUYyxY8eayZMn25ERHyMiVKL7/fff7f2Uay9UqJBZsWKFfeN4UxldASkKVALnjaNMO+uqb7vtNnPvvffakZPUYlSGtdabNm0yJUqUOC3PV0RE5FTbN+f/KrPGs7wNJsd6E0REomJ2FUkFjpL+/vvvPxvcd+3a1c6Ip/f7fqKxYcxnrF977TX7L9XowtGHmmbgjFaQgkDJ9gMHDtgnRxl3KoOHL7qfOnWq7XXN7DWL6fl/6ZMmIiIiIiIi8e2PP/6wfbRZ600HKApN79q1K9TCK97FPLBOacKcQHr27Nkp/h7WWrvU50xERERERET+r4ja6NGjbYFp0GqLVPRSpUoZF8Q8sBYREREREZGMrXTp0raSuKviqo+1iIiIiIiIiGsUWIuIiIiIiIikgQJrERERERERkTRQYC0iIiIiIiKSBgqsRURERERERNJAgbWIiIiIiIicdgMGDDA1a9Y0QaTAWkRERERERCQN1MdaRERERETkNBk2fnu6/r1uNxdN178n/z/NWIuIiIiIiGRAU6dONYUKFTKe59mf582bZzJlymSeeuqp0GOuu+4689BDD9nrn376qalWrZrJkSOHKVu2rBk8eLA5fvx46LHPP/+8ueiii0yuXLlMqVKlTI8ePczhw4eT/PurVq0yJUuWNL169TKuU2AtIiIiIiKSAf3vf/8z//zzj/npp5/sz7Nnz7aB9pw5c+zPBNxz5841l112mf23Q4cOpmfPnuaXX34xr7zyinn55Zftv76sWbOaV1991d7/5ptv2kD8iSeeMNEsXbrU/t7777/fPPvss8Z1CqxFREREREQyoPz585uqVauab7/9NhRYEzjPnz/f/Pfff2bFihU28K5fv74ZOHCg6dOnj7ntttvMeeedZ5o3b2769u1rXn/99dDv6969u2nYsKE599xzTZMmTUz//v3NhAkTEv1dAvfGjRvb3xmE2WpojbWIiIiIiEgGxawxAXXnzp1tQD169Gg727xkyRKzaNEiU6VKFRuAL1++3KaKEwz7jh07liAVfNasWebpp582q1evNnv37rX3cwn3xx9/mGbNmpkhQ4aY++67zwSFZqxFREREREQycGDNDDKBdJEiRUyJEiVCtxFwcx379+83Tz75pFm2bFnoQgr5zz//bO9fv369ufrqq83FF19sPvnkE5vqzZrrf//9N8HfK1q0qKlRo4b54IMPzIEDB0xQKLAWERERERHJoEjz3r17txkxYkQoiOZf0sMJrv3bqlevbn777TdbtCzyAgJzvPDCC6Z27dqmXLlyZvPmzSYShc0+//xzO5NNIH7o0CETBAqsRUREREREMiiKlVHJmxlk1keDf2fMmGED7gYNGtjb+vXrZ9566y0zaNAgW5yMC/+PX0G8bNmy5siRI7Z42bp16+xjx4wZE/Vv5s2b10yfPt0cPHjQXHPNNclWDneFAmsREREREZEMjFlpZpD92enSpUvblPALL7zQBt6gWNlnn31mZ5tJ5a5bt64ZPny4LVQGiqCxbpo11gTqH3/8sU0dT0q+fPls8P7XX3+Z66+/3gblLsvk+U3LxKYq0Edt06ZN9oMkIiLign1zrjUuyNtgcqw3QUQkKtYHww8SJWNYn8z7fqKxoWasRURERERERNJAgbWIiIiIiIhIGiiwFhEREREREUkDBdYiIiIiIiIiaaDAWkRERERERCQNFFiLiIiIiIiIpIECaxERERERERGXA+tnnnnGXHLJJSZv3rymSJEi5oYbbjBr1qxJ8JjDhw+b+++/3zYnz5Mnj7nxxhvNjh07Ejxm48aN5qqrrjK5cuWyv+eRRx6xTc5FREREREREAh1Yz5492wbNCxcuNDNnzjRHjhwxTZo0MYcOHQo9pnv37mbKlClm4sSJ9vFbtmwxrVq1Ct1PAE1QffToUTN//nzzzjvvmLffftsMHDgwRs9KREREREREMoqssd6A6dOnJ/h5zJgxdsb5xx9/NHXr1jV79uwxo0ePNuPGjTONGjWyjyForlixovnhhx9MzZo1zZdffmlWrVplZs2aZYoWLWqqVatmBg0aZHr37m369etnsmXLFqNnJyIiIiIiIkEX88A6EoE0ChYsaP9dsmSJ+ffff+0stq9ChQqmVKlSZsGCBTaw5t+qVavaoNrXtGlT06lTJ7N69WpTuXLlqH9r79699uLbunXraXxmIiIiIiKS0eybc226/r28DSan69+TOEkFD+d5nk37vuyyy2zwjG3btpmcOXPaNdjhCKK5z39MeFDt3+/fl5QhQ4aYkiVLhi61atU6Dc9KREREREREgiyuAusuXbqYlStXmrFjx6bL3+vRo4fZtGlT6PL999+ny98VERERERGJB9S2Ig4rXLiwyZEjh7n88svNTz/9FFqme9ZZZ5mPPvrIlC1b1k54Xn311YkKSY8aNcqUL1/e/v+VKlWyS3d969evN5kyZTKffPKJqV+/vi02Xbt2bfPLL7+YIImbwPqBBx4wn332mfn666/N2WefHbq9WLFi9s3et29fgsdv377d3uc/hp8j7/fvS0q+fPlMiRIlQpfixYuf4mclIiIiIiISv+imNHnyZPPBBx/YGlbUu2rWrJk5ePCgvZ847IUXXrCTnxSS3rx5s7njjjtC/z//38CBA81zzz1n614NGDDAdO3a1f7OcNS+4rJ06VIbXN91110mSDLHQ/o3IyQff/yxDarLlCmT4P4aNWrY4mNUDPf9+uuvtr1WnTp17M/8u3z5crNz587QY3h8/vz5QynlIiIiIiIi8n/2799vXn/9dfPiiy/amlYXXXSRnW2mUxMBM+i8NGLECLtslsubb75pPv/8c/Pbb7/Z+x9//HEzdOhQc91119lY7qabbjKdO3e2vzcygOdvEJ9RZJquULRVDoqYFy+j1RajH4xosI7aXxN95pln2lQD/mU0g7XXBQoUsLPMzG6TRkDhMvAGUSW8Xbt2ZvDgwfZ39O3b1wbsqgguIiIiIiKS2O+//24LRderVy90G7PJ1atXt7PPVapUMWeccYbtuhQ+8Zk9e3Z7/znnnGN/R/v27RPMYvM7zz333AR/K7ygtJ8pTEo5RamDIOaB9WuvvWb/bdiwYYLbGSnp0KGDvc4ISObMmc2NN95oR09ITWDUxJclSxYzdepUWwWc2evcuXPb/5c0BBERERERETk9M95+7FajRo0E90VOcIb/zJprHD9+3ARF1nhIBU8Ji+CHDx9uL0kpXbq0TUkQERERERGRlJ1//vk24J03b55N4Qb1rX788UfTpk0b+zMTm8uWLbOz2GCNNOnhZAzTiYnZ53Xr1oUen1HFPLAWERERERGR9JcnTx5z7733mp49e9r6VKR2P/HEEzb9u23btmbChAk27ZsltsOGDbP/D48ng7hcuXL25/79+5uHHnrILutliS7rplk/fezYMZtRnFEosBYREXHc6C1vGBd0i/UGiIhIItSoIov41ltvtRXAL730UvPFF1/YtdYgYO7WrZud0d66datp3LixGT16dOj/v+++++xSXCqHE6Dz+KpVq9piZRmJAmsREREREZHTJG+DhG2n4g0Fo1999VV7SUrr1q3tJSm33XabvURDEbPI5b9UH0/NkmCXKLAWERFx3LBXJhoXdLu5S6w3QUREJJh9rEVERERERERcpsBaREREREREEqGF8a5du2K9GU5QYC0iIiIiIiKSBgqsRURERERERNJAgbWIiIiIiGRoWbJksX2XJWM5fvy4yZz51ITECqxFRERERCRDy5Ejhzl69KjZvXt3rDdF0snhw4fNkSNHTPbs2U/J71O7LRERERERydDOOussG2Tt2LHD/PPPP3YGW4Lt6NGj9n3mvT8VFFiLiIiIiEiGlilTJnPOOefYCtjMZCotPPhy5cplChQoYLJmPTUhsQJrERERERHJ8AiuCxcuHOvNEEdpjbWIiIiIiIhIGiiwFhEREREREYlFYD137lyzePHi0M/vv/++ueKKK8wDDzxgDhw4kJZtEhEREREREQl+YE0AvXXrVnt99erVpmPHjqZcuXJmzpw5pkePHqdyG0VERERERESCF1ivXbvWVK5c2V6fMGGCadKkiRkxYoQZNWqUmTJlyqncRhEREREREZHgBdbZsmUzhw4dstdnzpxpmjdvbq8XKVLE9n4TERERERERyQhOut1W/fr1bcp3vXr1zKJFi8zYsWPt7WvWrLE94EREREREREQygpOesX711VdtM+1JkyaZ4cOHm5IlS9rbP//8c9OsWbNTuY0iIiIiIiIiwZuxLlGihJk6dWqi24cOHWr27t2b1u0SERERERERCfaMdYcOHaLe/vfff5vGjRunZZtEREREREREgh9YL1u2zLbcCrd7925z+eWX2wJmIiIiIiIiIhnBSQfWM2bMMNOnTzd9+vSxP+/YscM0bNjQlCpVynzyySenchtFREREREREghdYFy9e3Hz55ZfmnXfeMY899pidqS5Xrpz5+OOPTfbs2U/od82ZM8e0bNnSnH322SZTpkyJ1m4TsHN7+OW+++5L8JiNGzeaq666yuTKlcvOmD/yyCPm2LFjJ/v0RERERERERE5v8TKUKVPG9rBu0KCBXVdNy63MmU88Vj9w4ICpWrWqufPOO80NN9wQ9TGdOnUy/fv3D/1MAO0jgCaoLlasmJk/f77ZunWruf32202OHDnME088cZLPTkREREREROQUB9aFCxe2s8XRAmMCbAJbH6nhqdW8eXN7SQ6BdPjvD8fM+apVq8ysWbNM0aJFTbVq1cygQYNM7969Tb9+/Uy2bNlSvS0iIiIiIiIipy2wfuGFF0yskHI+ZswYG1xfe+21pm/fviZnzpz2vgULFtgZb4JqX9OmTe0s9+rVq03lypVjtt0iIiIiIiISbCcUWLdv397EQtu2bU3p0qXtGuzly5ebXr16mTVr1pgJEybY+7dt25YgqIb/M/clFVjTbzu85zYp5CIiIiIiIiLptsaatc2///67Tfs+fvx4gvtYd32q3HPPPaHrBMkE2Kzp3rBhgw24T9aQIUPMwIEDT9FWioiIiIiISEZ00oH1vHnzzK233mo2bdpkPM9LcB/rsE9nRe7atWvbf9euXWsDa9LDlyxZkuAx27dvt/8mtS4bPXr0MB07dkwwY12rVq3Ttt0iIiIiIiISPCfdbot2V/Xq1bNrmEmn3rdvX+gSnl59OixbtizU8gt16tSxKeI7d+4MPYZiavnz5zcVKlRI8vfky5fPlChRInTxf5+IiIiIiIjIaZ+xXrdunfn000/N+eefb9Jq//79dvbZ98cff9jgmdlmKo7TxqtFixamUKFCZsWKFaZ79+6mUaNGplKlSvbxTZo0MRUrVjTt2rUzgwcPtuuqKW7WpUsXVQQXERERERGR+JyxJrAlyD0VfvjhB1O9enV7wYMPPmivjxw50mTPnt220SJ4Zva5Z8+epnXr1jao92XJksVMnTrV/svsNQE2hdYGDBhwSrZPRERERERE5JTPWF9//fV2jfIvv/xiLrrookQzw8wwp1bDhg0TrdMON3v27BR/B2utP//881T/TREREREREZGYBtZ+0a9+/foluu90Fy8TERERERERcT6wjmyvJSIiIiIiIpIRnfQaaxERERERERE5wRnrESNGmDvvvNPkyJHDXk9O586d07ptIiIiIiIiIsEKrJ9//nnTpk0bG1hzPSmssVZgLSIiIiIiIhnBCQXW9JeOdl1EREREREQkozqhwPqRRx5J1eOYsX7uuedOdptEREREREREghlYL168ONWBtYiIiIiIiEhGcEKB9TfffHP6tkRERERERETEQWq3JSIiIiIiIpIGCqxFRERERERE0kCBtYiIiIiIiEgaKLAWERERERERSQMF1iIiIiIiIiJpoMBaREREREREJA0UWIuIiIiIiIikgQJrERERERERkTRQYC0iIiIiIiKSBgqsRURERERERNJAgbWIiIiIiIhIGiiwFhEREREREUkDBdYiIiIiIiIiaaDAWkRERERERCQNFFiLiIiIiIiIpIECaxEREREREZE0UGAtIiIiIiIi4npgPWfOHNOyZUtz9tlnm0yZMpmpU6cmuP/w4cPm/vvvN4UKFTJ58uQxN954o9mxY0eCx2zcuNFcddVVJleuXKZIkSLmkUceMceOHUvnZyIiIiIiIiIZTVwE1gcOHDBVq1Y1w4cPj3p/9+7dzZQpU8zEiRPN7NmzzZYtW0yrVq1C9xNAE1QfPXrUzJ8/37zzzjvm7bffNgMHDkzHZyEiIiIiIiIZUVYTB5o3b24v0ezZs8eMHj3ajBs3zjRq1MjeRtBcsWJF88MPP5iaNWuaL7/80qxatcrMmjXLFC1a1FSrVs0MGjTI9O7d2/Tr189ky5YtnZ+RiIiIiIiIZBRxEVgnZ8mSJebff/81TZo0Cd1WoUIFU6pUKbNgwQIbWPMvM94E1b6mTZuaTp06mdWrV5vKlStH/d179+61F9/WrVtP87ORWNk351oT7/I2mBzrTRARERERkSAG1tu2bTM5c+Y0efPmTXA7QTT3+Y8JD6r9+/37kgqshwwZonTxDGL0ljdMvOsW6w0QEREREZFgBtanU48ePUzHjh0TzFjXqlUrptskIiIiEs9cyAKDMsFEJD3FfWBdrFgxc+jQIbNv374Es9bbt2+39/mPIWU8HPf79yUlX7589iIiIiIiIiLidFXw5NSoUcMWH5s5c2botl9//dW216pTp479mX+XL19udu7cGXoMj8+fP79djy0iIiIiIiIS6Bnr/fv3m7Vr14Z+/uOPP8yyZcvsbDOXu+66y7bcKlCggJ1hfuCBB0z9+vVt4TJQ2Iwq4e3atTODBw+266r79u1runTpoorgIiIiIiIiEvwZa9pmVa9e3V7w4IMP2usjR460Pw8dOtRcffXV5sYbbzQNGjQwxYsXtz2tfVmyZDFTp061/zJ7TYDdvn17M2DAgJg9JxEREREREckY4mLGumHDhsbzvCTvz5Ejhxk+fLi9JKV06dLm888/P01bKCIiIiIiIhLHgbXI6Tbslf/LcIhX3W7uEutNEBERCUQLS6iNpYhkuFRwEREREREREVcpsBYRERERERFJA6WCi4iIiEiglldBS6xEJD1pxlpEREREREQkDRRYi4iIiIiIiKSBAmsRERERERGRNFBgLSIiIiIiIpIGCqxFRERERERE0kCBtYiIiIiIiEgaqN2WiIgka9+ca40L8jaYHOtNEBFHDRu/3cS7bjcXjfUmiEgyFFiLiEiyRm95w7igW6w3QERERDIspYKLiIiIiIiIpIFmrEVEJFnDXploXNDt5i6x3gQRERHJoDRjLSIiIiIiIpIGCqxFRERERERE0kCBtYiIiIiIiEgaKLAWERERERERSQMF1iIiIiIiIiJpoMBaREREREREJA0UWIuIiIiIiIikgQJrERERERERkTRQYC0iIiIiIiKSBgqsRURERERERNIga1r+ZxEREREREUm9fXOuNS7I22ByrDfBKU4E1gMGDDADBw5McFv58uXN6tWr7fXDhw+bnj17mvHjx5sjR46Ypk2bmtdee80UKVIkRlssIiIiIq4Y9spEE++63dwl1psgIkFIBa9atarZunVr6PLdd9+F7uvevbuZMmWKmThxopk9e7bZsmWLadWqVUy3V0RERERERDIGJ2askTVrVlOsWLFEt+/Zs8eMHj3ajBs3zjRq1Mje9vbbb5uKFSuaH374wdSsWTMGWysiIiIiIiIZhTOB9apVq0zx4sVNzpw5Tb169cwzzzxjSpQoYZYsWWL+/fdf06RJk9BjK1SoYEqVKmUWLFiQbGC9d+9ee/ExEy4iIiIiIiKpozXjDgXWtWvXNmPGjLHrqgl+WW/doEED89NPP5lt27bZYDtv3rwJ/p+iRYva+5IzZMiQRGu3xZhh47cbF3S7uWisN0FEHOXCSYCKxoiIiLjDicC6efPmoetVqlSxgXbp0qXNpEmTTLZs2U769/bo0cN07Ngx9DNBe61atdK8vSIiIiIiIpJxOBFYR8qfP78pV66cWbt2rbn88svNoUOHzL59+xLMWm/fvj3qmuxw+fLlsxdxrzImVB1TRERERETigZOB9f79+83vv/9u11zXqFHDzlrPnDnT3HDDDfb+X3/91WzcuNHUqVMn1psqclooXV9ERERE4kHl3lcaF6yfd3p/vxOB9UMPPWRatmxp079ppfX444/bKuFt2rQxZ555prnrrrtsy60CBQrYGegHHnjA1K9fXxXBRURERERE5LRzIrDevHmzueWWW8zu3btN4cKFbdC8cOFCU6hQIXv/0KFDTebMmc2NN95ojhw5Ypo1a2ZGjBgR680WEZE4NXrLGybedYv1BoiISIY9BkHHoQAG1uPHj0/2/hw5cpjhw4fbi4iIiIiIiEh6ypyuf01EREREREQkYBRYi4iIiIiIiKSBAmsRERERERGRoK+xFhERkYxj35xrjQvyNpgc600QyRC0TxAXKLAWEREREZG4pSra4gIF1iIiIiIiEreGvTLRuKDbzV1ivQkSQwqsRRykA4yIiIiISPxQYC0iIiJxJWhpn1ofKiLhNEESTKoKLiIiIiIiIpIGmrEWEZEMx4XZAs0UBEfQZuBFRCQxzViLiIiIiIiIpIFmrEVETrFz671qXLB+nmZERURERE4FBdYiIiIiGXzpAbT8QETk5CkVXERERERERCQNFFiLiIiIiIiIpIECaxEREREREZE00BprEREREZEAURFNkfSnwFpERETiiop9iYiIa5QKLiIiIiIiIpIGCqxFRERERERE0kCBtYiIiIiIiEgaKLAWERERERERSQMVLxORmBs2frtxQbebi8Z6E0REREQkDmnGWkRERERERCQNFFiLiIiIiIiIpEGgUsGHDx9unn/+ebNt2zZTrVo188orr5hLLrnktP9dpbGKpI161oqIiIiIywIzY/3hhx+aHj16mMcff9wsXbrUVKlSxTRt2tTs2rUr1psmIiIiIiIiARaYwHrIkCHmnnvuMXfccYepVKmSGTlypMmZM6cZM2ZMrDdNREREREREAiwQqeBHjx41S5YsMX379g3dljlzZnPFFVeYBQsWnPa/rzRWERERERGRjCsQgTXp3seOHTNFiyZcQ8zPa9euTfL/27t3r734Nm3aZP/dunXrCf39/478bVywefPmDPl8XHlOQXs+Gfkzp+cTG0H7DgXt+WTkz5yeT2wE7TsUtOeTkT9zej7x/x0Kjwn/+++/VD0+k+d5nnHcli1bzDnnnGMWLVpkatWqFbr9kUceMfPmzbOXaAYMGGAGDhyYjlsqIiIiIiIirvj+++9TVRA7EDPWZ511lsmSJYvZvj1hdW5+LlasWJL/H8XOOnbsGPr58OHDdta6TJkyJmvW2L00jI4wQMCbWLx4ceM6PZ/4F7TnpOcT3/R84l/QnpOeT3wL2vMJ4nPS84lvej6nBzPVO3fuNJUrV07V4wMRWGfPnt3UqFHDzJw507Rs2dLedvz4cfPVV1+Zbt26Jfn/5cuXz17ClS1b1sQLPkglSpQwQaHnE/+C9pz0fOKbnk/8C9pz0vOJb0F7PkF8Tno+8U3P59Q799xzU/3YQATW/uxz+/btbYDNCMewYcPMwYMHTYcOHWK9aSIiIiIiIhJggQms27RpY6fq+/fvb7Zt22aqVatmpk+fbtPERURERERERE6XwATW6NKli724jvT0xx9/PFGauqv0fOJf0J6Tnk980/OJf0F7Tno+8S1ozyeIz0nPJ77p+cSHQFQFFxEREREREYmVzDH7yyIiIiIiIiIBoMBaREREREREJA0UWIuIiIiIiIikgQJrERERERERkTRQYC0iIiIiIiKSBoFqtxVUXbt2NXfeeaepWrWqcd2///5rChUqZBYsWGAuvPDCWG+OZBCffPKJadmypcmaNWsgvkM1atQwEyZMMBUqVDBBwL6Nfdytt95qzjrrLBMEU6ZMsZ+5aJ588knTt29f45q///7bLF682OzYscMcP348wX233367cclHH31kzjzzTHPFFVfYn5955hkzevRoU7FiRTNq1ChTrFgx45Ldu3fbfzm+YtWqVfY58nxuvPFG46Jff/3VzJ49O+rnrX///jHbLgnOudycOXNS/dgGDRoYlx05csTuv8877zxz9tlnx3pzAkvtthzAgf/bb781lStXtiefbdu2DR08XVSmTBnz2Wef2ecTFFu3bjUPPfSQ+eabb+xJQOTX6tixY8Yl//33n3n77bdDzyfypObrr782LsmfP7/Jnj27Ddz4Drn+2eOgyHsQlMCaQPPdd981GzZsMFdddZV9j1q0aGEyZ3Y3qYrP3KeffmoaNmyYKCB47bXXzM6dO41rg1MEz4cOHbLPLVOmTKH7uM5+wiWVKlUyw4YNM02aNDE//PCDqV+/vhk4cKCZMWOGKVy4sBk/frxxCZ8z3h++O3y2ypcvb4oXL242b95s+vXrZ49PLhkxYoR54IEH7AAHzyPy8/b9998b1wTtuBqEc7nIY4z/OfPP4cI/d66dx7EvqF27trn33nvN0aNHTa1atcyKFSvsuRD78+bNm8d6EwNJgbUjNm3aZN555x178rlx40Zz9dVXmzvuuMN+MVw7+XzjjTfM1KlTzXvvvWdnDILgyiuvNNu2bTMPPvhgopMAECy45L777jMffPCBueaaa6I+n+eff964hGCA2Ru+Q5zUMEPK94dAu0CBAsY1gwYNskHo66+/brJkyWKC4rvvvrPv0cSJE03OnDnNbbfdZjp06GCDINfweevYsaMN1DihQa9eveyJ9ZdffulcBlLZsmXNddddZwdBcuTIYVyXK1cuO6tbunRp07t3b/t9Gjt2rD3xZDDbtYGCggULmnnz5tkZ6uHDh9vvEcEnx9ru3bubNWvWGJeULFnSbnePHj1MUATtuBqEc7kDBw6Ers+dO9c8/PDDZsCAAaZOnTr2Nmbkn3jiCfPss886F4jyGfv8889N9erVzYcffmiPP4sWLTJjxoyxx1hmr4Ni8+bNpmjRoiZbtmyx3hQ7KiOO+e6777yOHTt6OXLk8IoXL+716tXLW79+veeKmjVrenny5PFy5szpVapUybvkkksSXFyUO3dub8WKFV5QFChQwJsxY4YXRBs3bvQGDRrkXXDBBfY7dNNNN3nTp0/3XNKqVSsvb968XokSJbwWLVp4rVu3TnBx3cGDB70XX3zRO+OMM7zMmTN7tWvX9j744APPNW+99ZZ31lln2X1Djx49vKJFi3orV670XMQ++/fff/eCgvdl6dKl9nr16tW9t99+217nOebKlctzDdvsnwdcc8013lNPPRXa37Gfc03+/Pm9tWvXekEStONq0M7lqlSp4n3zzTeJbv/666+9iy66yHMNx0++/2jfvr330EMP2evsJ3jfXNWnTx/v3XfftdePHz/uNWvWzMuUKZNXsGBBb+HChbHePM/9BYcZzK5du+wok58GxZqP+fPnmxdffNGmtXXu3NnEO2bbuQTJBRdcYA4ePGiCIm/evKZUqVImiJgJ4fPHjBQj7sySMqpLejWziXXr1jXxLk+ePM6um0wOqXbMgPA++CPtZBZs2bLFdOvWzc70MtruCrZ97969Nh2PzAjWi5Ki66IbbrjBbj/r84KSZURGAZ+x1atXh7KKfv75Z3Puueca17DOdeTIkXYJxcyZM+2sG/788087m+0avjtkfTzyyCMmKIJ2XA3audxvv/1ml4FEou7H2rVrjYvnOsy48/3neEpGDvbs2WPOOOMM46p3333XzriD84UlS5aYhQsXmnHjxtn9BcepWFIquCMnm9OmTQudbHIA9Qv9+GmskyZNMvfcc4/566+/Yr25GUZ4IM2XmvV5Tz/9tLnooosSpaOQdugSPmuzZs2yxXyCkPbpF/d5//33bWBGCiiFpfgeNW3a1KaD8f5xIvfHH3/EelMzHNJveV9Ik2TdYbt27cxdd91lv0vhj7n00kvjegArqSCAA37NmjXtAJxv8ODBxiXPPfecGTJkiE1jjbaPc2FQN9w///xjC8ixzIpjpx9YE5Dy3Pr06WNcwhIXBj8YyOHcgJNPPPbYY3Z/x5pKl5ACznGIQo2s4Y38vLn2/QnqcTVIqLPA4AffHb+IJpNZ1C7Yt2+fTRV3rU4BxY8ZiC9RooRZtmyZXTr26quv2piB2k0uypEjhx3o4DmxvIJ9wyuvvGJ+//13c/HFF9uBg1hSYO0A1g1Q9OKWW26xgQAfnGgnCYy8uxIUsOaVwQK+CBRWoBgOo4WMrLlSFZi17eFrpPgqRa6ZcrXoxSWXXGIrsvJ8mKGKPKlxrXDM9ddfb7744gu7TpTvEGt3I0emmcGmUE5kQZl4RtElvkPMGuTOndueVHPQoTiJS6jWThEp3ptrr7026jopBj+6dOliT07j1eWXX56qx/G9crFQUXLPZ926dem6PRL9OMM+ILxuxPr16+3AbpEiRYxLkvsuufj9CeJxNSjncj62mzoS7Mv8rBW+P+z7Jk+ebMqVK2dcwzkCg4fUjWDQALxfvE/16tUzLipVqpR58803TePGje33iKCaAd9ffvnFZhwSD8WSAmsHkL5B2qfLqRvhOLCQhsdJAMEMP/PlYLaH0cG33nrLuOBE0k0uu+wy4xJmb5Pz+OOPG5cwI8UMKCm5SWFXSGFAihm5UIWeAwkj0AwEUJiI7xCzhoxIc6BxrfAIo88i6Wn58uW2tRZBAccdiv2QtcI+gAwDFwVlsC2IgnZcDcq5XOR5AEuOeC6g8wbPMalJE0l/jz/+uHnppZfs/nr//v32/Id9HIPuFHQlgzSWFFg7gFkcPkT+aFP4DA7tKFzbeTVr1symRPKcqCTJyQ07Y9a6knLj4swHARnrWSJ3vny9GC0M0roqF5Ha1aZNm0SDU7SgoK2Oaz14W7dubf/lQMLBxf8OMYvTqVOn0EmBxEZQerwmhZMZkGLoKpZVMWDNDBVp0sx28B2iVgnfI9oIuSRog23hM6IMFOD888+33QIkPgTxXC5IgtbeLRyp7Jxbt2rVyp57g04IzMST9RZLCqwdwEGRg2ZkKhcjgqSu8uVxCR98RtVJy2WwwN8Zk3JDqxAOpEF5j1jXy22upYL7vvrqK7s+DwQIqU11jTdBe39IY58zZ479vkR+h2hNFc/rkFOzrCIcI9HsK2i7RfsdVwShx2ukl19+2bYEopgczjnnHNuehgFe1zAjzfo8CpiFf4eWLl1q11uzv3BJ0AbbGPSkPRA9348cOWJvY2CUgQJaH8VFW50MflwN4rkchf+SCkRdm8QKWns3V6gqeBzj5JhxDy7soMJPlgkESFdxbd0UOCD6Mx7hKEbALI+LkhqfIqvAxSIljAQyk/PTTz8lWGtE711md1xL201q/TsVc13sv8mMaDQ8HxdnEalmTHrXzTffHOr5zHpDv/cmQU7//v3t7a4E1/RGpgCWyz1ew9HLdejQobYYlr82j5kp3hfWtPXr18+4hMCGdYfRgoW///7buIZCRAy2RX7/CXTYn7vmoYceMp9++qnNNgr/vHE75z9kFrgmaMfVoJ3LsQ+jAC1FMqMFoq6ZMGGCXdpC/ZKg+eijj+zxyB+gYiCHziHMYMeaAus4xgGSL7Zf6CISt6e0ZiceMXr25JNP2hRc/3kQEHACTZEpl/hVgHkOnHiGV//m4M9aj2rVqhnXUCQqX7589qBPGyrwHlH0i/s44XGlWIz/HaL6N0Wywt8fUgxdPOiwzaR2UvUTPD+/snnz5s2NazipZPScauA+ijVSEZgRd7/wHG0FXQmsWbtLGydO0Ji9Zr2ry4WKaE1HwZjwNm8UiuHYxHviWmDNoHR4kSIf7SuTK9QWr4I22Mb5AfVlwgc/brrpJjvwwXHIxcA6KMfVIJ7L+fs4Oodw7AmCoLV385GxwrkO2UZ+NwqWXbVv394O6vD5iyWlgsd5cSzenkaNGtnRmfBelBQiocCKv3N2CbMbpK39+OOP9jrrI0gtZKaKE2iXTgL8FC7eqzp16iQoEMN1TtoYYQ9vs+MC3oN58+bZkfRwvGf0Tqf1hAv8gSf+7dmzZ4LPlv/+ECi4VtiHYl8MFJDizogtI+xUNGVmlFkrloi4hAEp2mkRPIdjnSifQbJ1CIJo8+RKmnvQChWRecN7FFkZl88d75FraZ/MTNEGjTZv7MdnzJhha2XQnoYTM2Y/XELQSRVmBts4oea9YvCAAIfBHdYfuoS11KTlMxMVjrXwpPG7sh8I4nE1iOdyfgcesiJcO1/LaO3dihYtatvtEUiHYx/HZNf27dtNLCmwjnOsn2ZklsJL/gL9oGAHxsGfVCJahTEy7WrqzR133GELeDAaHQS0a5k+fXqiKtqMCrZo0cK5VEl2uHyHgnRwYd9AqjTr2vzvEP1rXeuZDk5keH+Y/QhHn2HS2QjelixZYmdImBGR2GR/0OeVXtbhCECZ5XVtBp5TH4LrZ555JhSkMcDG7Du3uSZog20MdjAgwMCHP/DJWmtqLXDi7GLhpaAdV30MFoQfh1w9l+N7v3PnzkT7OFcFsb0byFpZvHhxogEQBuIZdFMfa0kRo34///yzE22AJBgI0FauXGmLdZCOC4qU0LKqSpUqdt2oxA4nyqThhqe2+8E2QQ6zHy6hRygzbsxIczIADpzs9wisCaiHDx9uAwUGsFwSlEJFFPShqBeVmfnsgc8amQRU2HatpWB4kSzSBwkKKPzn2ixb5PeftNzwAWtXB9sI1Kg6zZIdngeYGWWfR3aBi0UBg3ZcDWK3DYqXkenBvjoyEOVY5JKgZU357r//fpuVQ0p4OOp//PXXX7ZmSywpsHZAy5Yt7c4rfP2hy1iLHA2jan4FYA6o8d5Wg0AgtVzbITNyzkFx2rRpoYMmB0tOrJn9ZeQ93nFwJG2Q9EiqaCc3gk4FUJcErco5WHdID0q/enH58uXNvffem2gNrCuCVqgIZAswwMHacZCmyxo3qoOLnGpkErDmNfzz5upAQVCOq0E+DpF5mFJqtcRet27d7HvBLLyf/cHsOzVzaE8cPiBCynh6U2DtSBEcRpbYITPKGVkEhxQilzAjxczT4cOHE5xwElST7v7HH3/YVA/WLUeuuXRpJxyEHTLvU/hJjUtrjzhRoco0JzAprS+MXKvjQnsq0iEZMIhMhWIfsXfvXuNS0aUbbrjBVviM5+/7iaKXJu8DxdciCxWxZMS1QkVBwGAoBdh4/VMaGHVhMJRMgSuvvNKeSHI9Oa6dJwSZy8fV1ByHyJho2LChnT2U2AtK1pQvtdvPZEoslowosHZk55XcB8e1UUFmpaZMmWKDTX+HzLoWRpr82XkCIkZDp06dGuvNFYkbfjBAMcOrr746QQoe+wHSJwlOWcfnErIKqKAfpMA6CIWKyPioUKGCPQZxPTmkUcc7BkPpxU0aIWt1k8ticWEwlPdl27ZtdnYwCOcJGigwTnXboO4F+7ekum24MDgVZEHMmnKBAmtJd8xKc+LPyFk4vvwcLNkZsL6S6wTckj6opkhmBBkRfhuxpMQiveZEnUjVWFdSC/0sCWbgCbLDl0v4Vc7vvvtuG6i6hErMBDuRxctcFoRCRZGBGyfT0U4ZXAncJL4FbaAgiMfVIHfbABktDAjQIYA0/XDUk3CJsqZiQ32sJd1xQhktRYjb/ZPNQoUK2QqgLnF9h8xght8LletB6QOfHIIEl07S/Fk0v41b5LIQV/Ee0JebwjFU9Yx8Xq6ccIYjeL7nnnsSFSq67777bLaBC1iW42cVcT1IaGP58ccf22VH4TgRZZbHtarTQSgkdfz48ajXXRa042p40SuOQ0HqtkGXAJYk0VOcAqF0CGC/R7G8WPdGPtkUcLKmwtvyUg/jxRdfdK7A6U0OLeNRYO0ATv5fffVVW+mOLzlVJVm0z06gTJkyzjWzp8Ivad+0NAivANyjRw87wub/7NK6oyDskKn6G+26q4LwHIJWzTMppLBffPHF9npkyrGLbVvAPptghn1cZKEi0pFdEN6JYsOGDclWoneta8W3336baAAUDOjOnTvXuIZsFop+RhaSYskB97kQWAdtoCCIx1WXa5OkhEFQahoxsPbCCy+Yjh072i4IXCej0jUsqaCWUSRui9yPx7vcuXOHzgXifkKBVHCJb4MHD/ZKlizpjRw50suZM6f3+++/29s/+OADr169ep5r9u3b53Xs2NHLnj27lzlzZnvh+t13323vw5IlS7wffvjBc8X555/vffLJJ/Z6njx5vLVr19rrzz//vHf77bd7rrnjjju8vXv3Jrp9//799j6JvVGjRnlXXnmlV758ea9MmTIJLhI/fv31V2/y5Mn28ttvv3muYj+9ffv2RLfv2rXL3ueKn3/+2V4yZcrkzZ07N/QzlxUrVtjjbenSpT3X8Hx27NiR6Pbly5d7BQoU8FwTlM9bkI+rR44c8fr06eNdcMEF3hlnnBE6n/MvruH8esOGDfZ60aJFvaVLl9rrnHOfeeaZnmvatm3rValSJcG59OLFi+1t7dq1i+m2BZkCawew05o+fXooaPMD619++cXJA6aPIJqDPhc/oHZV0HbISZ3U7Ny508uSJYvnqj///NNbsGCBN3v27AQX1zz11FPeWWed5Q0YMMCe0PTq1ctr06aNlz9/fu/ZZ5/1XMXna+HChd7hw4c91xGoBUlSgRuDBXnz5vVceh7hJ//8HH7JlSuXN3r0aM8VNWvW9C655BL7XKpXr26v+5eLL77YHn9at27tuSZoAwVBPK726NHDTiqMGTPGngO9+uqr3qOPPuoVK1bMqe9Q+Ln2okWL7PW6devaQTZMmDDBK1y4sOeav/76y7v66qvtdylHjhz2wmewZcuW9j45PdzKBcigKOZFT9doXFuHHLkGtkqVKiYIqK5I0ZVSpUrZ1KFZs2aZ6tWr26qZLhXwoODX/xtwM4cOHUpQAIx1yF9++WWiVEMXbN682VaaJ2XVL8AUnmLsyhrroKassa6VKs0UU+F9oW0Yy13oY83nbdCgQcY1lStXtrUiWMt22WWX2YuL+zt/PRvvC5+zaJXoSRF3BUt0+P7z+aL3aXirIPbVfN7oSOEKf70+6/cbN26cZCEp1ypOc2natGmSFaddEtTj6qRJk8zo0aPNFVdcYZfB8X7R2aFcuXJ2X86SP5ewFJE6H7Vq1TIPPvigadeunT3Wss+gd7KLBTTpwBOU9m4+eqdTY4alFTt27EhUUDPW53MKrB1AuxMCAr9cvo+WO5GtXFzAmq9nn3029KWILFLiQqGvoO6Q/YJfXDjxjMTtfkVQl/Ce8Nz4bBHw8NnbtWuX6d27tw1GXbNly5bQmmTWG/l9q+kH7WJlbSrLUtCQ9dV+3QX/e/Xoo486GVjz+aLewuzZs82YMWNs3YUzzzzT1K9f3/Z4dWW/4K9n4+SFKvSRlejvuusuW4neFf5a8KAUxwpaIamgDRQE+bjKPo4gGhSV8ovP0meYY65rnn/++dB1vktMlNDFgUCUVrCu4j3y36cguP322+1EFt+Z4sWLx10dFgXWjhw4KT7CKA0nA5999pn59ddfbeDGddcwisnOisIX8filyMg7ZAJOTqCpmMvATcGCBROc1HBSGl5h0hUUI2Lgg5MyPm+cBFB9mpkpAjdO4lwSlAwJ37Rp0+yFQcRw/OxqNWpmCxgY8AsyMgv/1FNP2dYnzCK4ElgHqRJ9kPskB6WQVNAGCoJ8XGWQgKKGHIfYVzODzcAo363IavsuqlOnjr24JIjt3SJxbs2FSZJ4pMDaAaR7klL4xBNP2C/LY489Zk+iJ0+ebFNvXEPaE9WyL730UhNULu6QQboqCGboN55cL1GXULmY2ULQ45lBKkZwCUojq1C7ICgZEj5m3OljHYkZEBcHCsBnzJ+x5sIJKO9Xv379Qt8zlwShEj2zoX6f5ORanrnSgo/nwf6LfRop7ckNUpMd5uJAAYOGq1atstcrVapkZ7FdPq4ShAZhMsF/j5YuXWqzcMj+YhKBtoksUaTriwtSGmBzbbAtiO3dIjFhFb6cIt5kYqF1rDdCMhbWizOyGa+jTSeLWSi/JRoDBwSmr7/+uh3VZZbERaQcR+vL7VoPxP/973/m4YcftgEp7elYjkCAw/vDOkta2LnMH8F1LUPC17x5czvQRvBGgL1ixQo7Y9W2bVt7kka/YdcwKEXA06lTJ7smlJkcVwcJfPQRpUdotH2Ci0t4XPfOO+/Y2hGse2e5QXIBm2sz2mvXrrUp36wN9dP3GZzi/IFZXxfXiRJ0ksnCQGg4slj++ecfc//99xuX8f6QNcU6a1fqSaR28sCVwbagOhgWSC9cuNCmgdPm9qKLLrIZSOFy5cplYuo0FUWTU+jGG2/0Pv74Y9vaIAimTZvmtWjRwtu8ebMXFG+//batvkp1Ziov+pXb33jjDe+KK67wXLNp0ybbys2voBtZSdfFz9ykSZPs9TVr1tgWVTwnKmt/9dVXsd68DO+nn37yihQpYiuY0nqPCucXXXSRrcRKuyoX0VKwXLlyXu7cuW1btCeffNK2dzp69KjnoqBWopf4VL9+ffu92bp1a+g2rjdp0sRr0KCB5yJaIc6ZMyfR7fPmzbPVtV0TrXWYxI8gtXfLFKWbQ2R7t3g5P9WMtQMYaabCIiNrFCditJPiEK4iZY0ZQ9JVWOsaOdrkWsoaGDUbMGCAadWqlZ1xo1IuM9VUaCZ1zbXnxOeMEUJm4KMV+3IxHS8SxbKYPXAxLY8UUL/SNJcgZH/s2bPHvPrqq/a7s3//frvchRkcF9cehiP1mFRw0sK5kNHC7Dwpri5hForvPkuT2MctW7YsQSV6Zk/j3YgRI1L92M6dOxuXkEnAeuRrrrkmwe3UYSG7gGOTSyiSRyorx9ZwZLPUrl3bVtd2De8Pae1lypRJcDv7BNLcXXtOVGxnP+0fh0gJD8La6qCghgxLkiIrznMuV6xYMbtEzhWzZ89O9WNjvdRKa6wdwAkL6ZAcIMeNG2fXeVD8ghQwUiVr1KhhXOJiFeaU0AIkvJpx+IGUQQTXBK3YVzThBWRc88Ybb9ggjXXVrKnmZIaTGv8Eh5Md17AGvk+fPiZoSEujKjD/sj84fPiwXZfomiBUog8vMpkc9nmuBdYso3j55ZcT3c5n74EHHnAusCbVe/fu3VHrLjCg46JzzjnHLFq0KFFgTWorgY5rOA/gOPTtt9/a81TeGwZ5OQbR+YBBOEl/QWzvdplDdUkUWDuCNVStW7e2F2Z2WGM0bNgw89JLLzk16uTiWq/UYA0YI+n+WjAfO7DISscuCFqxLzI8kpuZ/vrrr41LOGHxT1rYHzAQMnHiRLuOnM4BLq4F46SMGapoLfhor+FiCzFG2ZmBZ+CDdf633nqrHRSpVq2acU0QKtG7WmE+NdavXx913TGZUy4+byoWd+3a1RZtpegfCErJDGOAJDxYiPmaylSiNR3Pif0zgSfIBmNf4WJ7KvZjXPxtZ183dOhQM3z4cLue3MXjUBAEtb2bj/MdBqn9yaz333/f1pigRzetfGPduUKBtYPpq5xAjx071vz8889OpoBS+CY5nLi5hgMjaauk3DFKyIwU7xPtdV577TXjmgsvvNAOFDCyTtodJzLsyPxibK5htj0cyxA4Cfjxxx9t+zcXbd++PVRxmgsnz/6stWs++eQTGzwzuk4QGj4IwnUXA2v2cx06dAhMqn7QKtEHDQOgDHqSZRSOwowupuf6FZgZQPT3B/7KxciK7q4EcCyl4hyBvu9kIYLjKllg3OcaXndmrf1j0Pz58+3yqttuu83J41BQBLW9m48MHAbcQHHDjh072vM4sid69Ohhz1NjSWusHXDgwAG7xpo0cE5sSCeisjFp4ARArmGteHKzh64cJCNR2ZPRdNLCQY/u/v37m3vvvde4hhYUzAiQPkh1Vk5kfvvtN9v27cMPP7Q77CCgqiQBKpkfLqEyLqm5rNVl5oMq7QyAuDJzGG39LifQpBQHoW9tRuB6JXr8+uuv9sQzWpVzBg1cQo9xBqjeffddU69ePXvbd999Zwd3GBR58cUXjUtcWlN5ogiq6WsPvj9kJLrIX+LC58s/DtENReKD32PcxToyKX3uqOvBxA8BNoM7LJWlwwvnEZwbxZJmrB3AWgg+SAQ5jETVrVvXuIwvROTsIYVwWHs9aNAg4yrSPLkQkFJ8ybU1LEn1ayToYVTQ5WJfSWnTpo2tUeBaYE2qNGOifrpXSoNV8Y7BDda0KqiO71R9vv++OnXq2IurOBFjaRXH03nz5tlUfQYRWTtOezTXkB1FUSKCG78gKEt6yPZgANE1rgXLJ4JAOrIom4v4/rM+nDXW/vGH941lI/Fg06ZN9jvNJEdql+QxscPkSLQlSa61GSWw5pIU156Pj/2bX+iPyUYmGcE5N23rYk0z1nFwQknaAjObyc0eNm3a1BaOCjLWIzP6xCi7xBZrKTkpizxAbt682e7UihYtaoKAgJoBHQ7ArmHdu5+CRwoUB1B/Brtv377GtboLbPcdd9wR602RZIIBjkOkgFN52vVBENaHk7JK6qDfyYHZNtbBchL+2GOPGVfXWpP+DZYgRNb9cAmD1Ay6RwtyKJrnImoTJNUL3rVaH+A8wU8H5zhEOjiZbezP6XufXhiYZV0+k1AUiCRTg+V4/gA0nUzI6Ehu/S0DbEyOcD4QGRq52Mc6Wo/u8AF4156PjwwpJuTIzGEyjoEQ9t0E2XwO/GyQmIl1v6+MbtmyZanuu7Z27VpvypQp9sL1oFm9erWXK1cuz0Vbtmzx2rZt6xUvXtzLkiVL3PXVO1GNGjXy3nzzzUS3v/XWW7a3qGtq1qzpXXLJJaELP5coUcK+V8OHD/dcRp/KqVOnerfffruXNWtWJz9v9EGmjzW9n4cNG2bfk/CLxN7XX3/t3X333V7BggW9vHnzerfddps3ffp079ixY56LONasW7fOXuc5rVixwl5fuXKld/bZZ8d464R9Gu8L/WrZr4VfsmXL5rlo1KhRXs6cOb0OHTp42bNnt9+hSy+91MuXL5/XvXt3z2V8b1599VXv+uuvt+8Rx9b0xHFv+/bt9nqfPn3s8Z191oEDB7zvvvvO9gl/7LHHkv0dF110kT2P++2337x9+/bZfs/hF9dEbv/ff//tffPNN169evW8WbNmpfv2TJo0yb4fabVhwwbv6quv9qpUqeK98cYbodu7devmdenSxYs1zVifZsw2J2fdunWhKpFJoeovMzmss/bXUDJaw1oC1oH51ZtdEVlVmo8gM6TPPfecTcMjtcg1V155pX0OFPUh7SgyLfeqq64yLuEzRYVmKoGHYySQ4kWkhboksgImI7n0UydtjUqSrpkyZYqdISAFj5k23i9SWf12W35bJFdEtp8Jx3eJ/aTEB449HNeo+cHnkNnem266KWqrp3hGNs4XX3xhZ3WrVKliW72xNITK0+zP/XZiEhsUyWRmimw+ZkCDgF7VFCojQ8fPkuB5PvLII/Z7RUVtl1D525+p9lttkV7MMYh/0/N945jOORjpwGwHxeD8FGF/6QddM6irkBRmsyna6mo7t9Qiq6BTp07285eeMmfObD/37GfJDKIuzMlkSEyePNkWao3b5ZaxjuyDjtFWRtL4N6lLSjNM7dq18ypXrux9//33odsWLVpkb2OWKiivSe3ate2op4ty584dmvEIAmakyKaItHTpUvtcJW2ZGX/++Wfo588++8xr0aKFHS3n32nTpqX4OwoXLuzdeOON3ssvv+wtX77cO378+GneajlRZcqU8Xbt2pXodmYNuC8I+OxVrVrVySyJm2++2Rs6dKi9PnDgQK9o0aLefffdZ98bZt0ktpjFDVpmHrPV69evt9fPOuus0DGWGVJ+dg2ZXz179rTHMPZrscQ55I4dO+x1XsvIc0led17/5DAL+vHHH3tBx7lqnjx5YvIePfHEE1716tXt9QsvvNDug6MdJ5OTI0eO0PcoHql42WnG7OWIESNs1cRoWD9E8aTkTJ061Y6s+z3bwKzhyJEjnZsJRWRPTX/2MFZr9hjBpJog61Op0MmsLLMvVO5kxNPvN5kc/r/wvpquY/aT9Ur0BwxvdULbLb/ibJBavKVnuzc+UxQTot0FleRpE8G6VXptU3mdIoWsTQsfbY/EmsMg151gfxBtfZhra12jZSKxX/nzzz9jsk2n6v2hMwAz1uw3yY5wreK0P9vmF8ChJgHZYMzkXH/99c7VKAgi9n8zZsywayaDgtokFAFl3TsXssKqVq1q62O4uN6V7Y+3zDSqlHPsoDJ0eNec3bt3p9jfmO8+NRfIqqS4nF8EMFpRVxczZv3s0FdffTVmhSfvvfde069fP7NkyRIzevRo+5716tXL1u2gDR3ZQikhFiKzIF7rRygV/DTjw1KtWrVQz7VIpGJQRCWyMEc4ijFwwCddLTIoJx1i3759Jj1xYkj/X3rjRaYKUzSCwhyu9J2dPn26HfTgNeYki3R7CtrwWvPVIM2Jg3u09lLhgTTp6+wgCJii7ZDZ2btWuZ3nTCoXnzHMnTvXnhRQYMW1iqapqZrtFzk53Sc4fBb8frP01yawDj95fOedd2xBtcjq+UFGER9S9yjkyD6EAQY/RZKiJPStTE981jlp5CBPWiEB5LBhw+x+miU4N998c4onM7SoY+AkfKkOny2KF7HfSS4lMR6x7IhgmuUHpO4T+FDoh0FFkdOxT+C7xoA7AVLkMTW5gq/xivRXggG2ffjw4Xb/Rso0yw/8pX1ycpgACT/Gs2+iv7GPVo7se9l/JSW5wdwgFC/jOTBozWvFYCite9N7e7b9v3R9H8d7iszx2WdJARMbkZNvkXg8xSV79uxpJyYjB0xYchFLCqzT4QSNPtTNmjWLej/3UVExudYSrDPicZzU+NWY+XCy4yAgZL1BeqF1Aa1ImI3hS8oXlJPHYsWKhWYzmIlLaQdE9T5ah0Wr9pmeBxdarVxxxRV24GP8+PE2wGHtCa1LwLo7TrCpWJ5SsOYHZtG4tkMGQTSzOn6Ax2BDly5d7ICKa9gRs76KAM0fqaUHLyO3rO0PH9k+3b3hGazg889MH99nrocPmlHhkmAuSBkQKWHdIVk5ZERQ7ZfPHIH1xx9/bAer2Eeml/fee8/WtKDNHLMeY8aMsT8zW0FnBj5LZLQk1Z/eP5lhXxB5eCU4YECFkxoCb5ewX2dtHAF1ePaUq1LKYjndmSunAwP1o0aNsvsQjqNkzNGnm2COQTyXDBkyxPbm5uSf/WT4sZXrDHa5hvWhnAv4fas552DShMEp9id+DR059ajTwesbL63AMqIsWbLYbiZJrY0mvnj77bdD598nWu08vSZHUhTrXHRJGWsJqH5HFcly5crZC9dZ20Z1vPR0ww03eFdddZW3c+dOb82aNXZNynnnnRfajm3btqW43q5v3772MXXr1rXrRFu1apXgkt7ruHgeoLot1SxZR+xjnQ5r76L59ttvU32R2KpTp443efLkRLd/+umndm1/eqLqKNWvwef/8ccfT3D/c889Z9dbZyTnnnuurdwK1n79/vvvobWHZ555ZrpuC6+9X4mcqtfUFPDX4uL111+3a8NS85zYTwaFq9W/U6r1kdTFNdRmYO0ha8fPOOOM0HeIz27Lli0911ARfMSIEbHeDAkIzk2ppZCR8HyjVeE+ePBgTF6LTJkyhSq3pzUmSu4Sa5qxdgRvEzNbq1evtj9TyZiZ1pTSW081Ro5Jp2FGzXf//ffbdeDMQJOSkdKMNb+DtMpbbrnFxBppmkuXLg1VgQyv1AnWPtHT1F+Ll1GQkpMc0tdckjNnTrt8gfcyHN8nlmKk5/vL+lrWsDMjxiwSNRiomVC+fHm7vp9emswykaniArIAqArtLxk42fT4n3/+2aYYh38HWUfFmv70XO7CPoxUfX/9FrMc7CP85Q9k63B9//79JqNhtoHP66pVq0Ipd2T5+BlLLuHzFo6qzCyvYhkGvVFZb+kS9iX33XefTX8N/w7x2aUWC++dS5jVYl8Yb0sN6HHMLDqzZnxmqDbN8jiyEl3M5soo+D6QJZbSbCbvJ5lT4fs4st1cOR6nZoaY9ebclt4zuxs2bLDnPekdt6Q3FS9zBB9EUrC5xBIBSNasCT82rBXiIEM6+9ixY1P1e+IlLY20TNZz+oE16cHhKYDsCEiny2iiFWwL3xnGPNXmBBFQk/L9xhtvhNbqcVLEbZHB9unGSRlB/rPPPmvbFflr9AnYWJrw3Xff2UDbFXz/Cbb4DrGGkFYyJxpoccJDKri/1tz/rJHOmt5FVgoUKGA2b95sA2tOSkjfZDDED6w5seYxqREvS15O1WAb6fDsM/mcglR9WgTx3jFY5JJoSz6oh8LAMEuDXAusCQQYbI+UP39+59oj+gN27FfipQUVNREInllCwDIRloe1bt3aDsIx8cHgoJ/WnVHw/nCuEOs1raltbZsSCgJ369bNHsdYEgiOxwwcMxmU1PKfeJXU8sSVK1fGZBCodArFxlhKSp2V1NRP4NyJ94v12NRBohYL/y+DiakpgHZaxXrKXFLfWJ2m7qRHceH6xIkT0307LrnkEu/dd9+Net/999/v5c+fP8U0uqefftrr3r27Fw9oLp9ce6M+ffp49957r5fR7N+/P8GFVhrffPON/dzNmjXLc82CBQtsCw7S+ps2bWovXOc27nPN3r17vccee8ymuJ9//vm2RVD4Jb3Tu/hMdO3a1b6e2bJl86655hpvypQpqU4fnjNnjm3x1qVLF5vOSguXZs2aebly5bKtBdNTp06dvPLly3vPPvusfX1pd0h6+MyZM72vvvrKtgq54447Uvw98bTk5VS11uE9jvTggw/a40KQ2uHxuXMNSw/4fEYup3jvvfe8ChUqeK7hO8JSrbJly3rXXXed17p16wSX9Hbttdfa7aBVUbdu3bxKlSrZ244ePeodOXLEXmdfkZFUq1bNy5Ili23/yDLBl156KWqbTlda23Is5ZwwEst/uM8VHId5T3i+/nX/QvzAe9a5c2cv3ixbtixVy3Defvttu0SsV69e9nzB39fx3l1xxRVerCkV3AHMbFFxmhSv8MJLzHhQtp5S9enlmWeesQXZkhodZMaJUaTI2RlG/MIx2ksqSrRqn1QVl/jEiDwjuaRVuYbUXQrt+dWYmammEBMFAF3DTAn7AGaHyaiIHJVmeUYsKn2SBfDJJ5/YfRNLRlj20aFDh1AxsORQcIl9HZ8t3itS9KmaSzua9MTfZtaCSr3MzFKojCJ+FDLk+ZGZQ7uppAqwxOOSl1OB6syk5kd2giDjh/fIteUyzDRGa0VDFsvevXttFolLKPJHgVOK7dG6j1kcZle7du1qzxH4TLuEfUZyKHKUnvi+k4HCZ51isqTbk8XhZ2rweaG4H1luGQnZELwOXOiiwv6b14blYnRZSc9MsNS0tk0u246iciwRiTxWUVSLjCUqWLuAziLsz+g4wjEovDMFS5vIOopFu63PU5FVwP4qpYxI3osBAwbY1qThy14oetq4ceOYtyNVYO0ATtDoKcxJdOSXhxNP0idcP0jG8oApqceOi2AjvVu8SUIcKDlxpvd6rEVroQFO6gmwOdEnfdq15QOROKkisOZAntr9NmmEQUkNpZougwxUbQ9HPQBOhkifd4nf1SHyFIhlGPRXPd3dAU41ngfBNYPffkcBTqK7d+9ub5O0IdWbmhz+UjH2AwRr/jIy9nEMOqVmgIkuBwwk0hmAmg4M5DBw5XJV8J07d9rAmi41VDtnwiQ9O1ucita2fOdpFUuXinAM+L7//vs2hdolvB+cr0VOXsXbPjdcaqp6UzOH7yKp5eGBNTVq6LAS60FerbF2AIUx/DVt4bjNlRE0BctuiRxZ9GdzaE8Vi5HOU4E1OazBYVQ07tbknCDWMEf2bow3nIAyqvz444/b2WvXceLLJbWYIXzttdds26AgYN0hWVOsafOPRxSXIpgjsHZNZK9UTvro8Xoi73E84YSUrAoKLTHDRuYFa19dzMiJR8yIMljoB9ZMdoQPJjJLxnr25FCzgQCQgJwAj0CA4w8ZBRSaIjPGJWRI+DPVvDYM9DJT/dVXX5natWun67bwuSeTICnMQlPvIjkcr8gwIiuTopn+Po7zBQYLXMBgBoNAoC0ig8FcovEfl16KFy+eqqyClBBQkz0VuWabTNj0rpkTjQJrB9CvmhF0Rs0ig9WgpBm6iArGqa1umJrCGfEksscuz5OTTgqV0IPXNcyaEuiQxs5B3z/QcDLNCZJrgTUFfTgZoxgbJ3yxxMGNk8Kk8Nlx7fU9FajGTOooHROCsOSFk858+fLZgQJOYv0Tpb59+9pZUdekVEjHVcx6xkMxqdN9fE3vY2rTpk3tsgc/9dsvbuUj+EopKHjwwQdtGi4BXnhRVFJaI3+fK+emZ511lunZs6ctNpfegVq4lLpSMBDNMp6Ulljx/pA+PXHixFAHHpZdxUvB3ZQwg+tXAmdQLdp3KFb9nmvUqGGWLFmSZGCd0my2j88by92OHj1qH8+xlveL/tcMZseaUsEdQEBAEM3Ipj8K+P3339tUItZQhJ+wESRI+lVD9u3Zs8e+9uzcw9fBM/JJWlHv3r1juKUS72tyThSDHKTjM0BAsBMZtLn2fIIo3taInkqkroLPnquSShmNJjVVamMhsnZJclwbyAk/voa3Q5s2bZod2GEZXDxhxpY1uiwBSW6/zQwvwVr4cYiOEAyGpGfqdFII8MkySk1nB2qW+DPWdE5gxprglQF4zlXjJQU5I+G9YLad7j1cT05KAw2n2ty5c21WAdX1o+E+lkmkZrv47HFORxwEBqrYT8dD5XYF1g6gEElqMNrz9ddfn/btkcTIHGBEk5G0cMzuUNTEtZOaoIn3NTknivoKyYmsxyAiCZEmyQwky6n82WsKT5EKHl6gjeMqA9nxKCPWLhk1apQNGFjzGk9S0yqIVHEG3CMDa9KNae+WngOikcX7fKxTpm6Cv3Y8tdkPPH+CbPpAU9yRzAmWI0hsB3tY8hY5a03YR02A8Nayrjp48KD9nKVUTDQ9KbAWOQU4SNKbOLKaJIEbBTNcOMCQ/ZBasezBSxA8adIku46QUUoqsabUV5h1N88//7xp2bJlghMaZkXefPNN+96JnA5BKVREeuFDDz0U6ssdeergWnE6UgapWswglT87Rx0JglX2E35PdYkvfJcI/uKtgCbHlIsvvjjZ7wEZBqROs86U4xDrRAkICKo5lqU0YJpehaT821OTLsz+zJ+1/vbbb21WASnIZO8RZEvssETLTwsPt3v3bnuba/tsV2iNtcgpQGDHWsrIliakraUU9MWLyBMVCkGQ2sZJDDhgsqalSZMm6bpdjJhTXblgwYI23Yw0oV27dtlZJU6ySAciK4C1Ua6uyUkLggGeVzjXRqL57FFDwg/aIiu3ulajIIiFiqiWy2eN1o/RWry5hqJrX3zxRYKUV67TbqtFixbOBtYMOK5atSq07/RnHoOA/RyzwsmlW8eyVVBKyGBjrTZZUmRK8J0ia4IuD++++65JT2wD9TleeOGF0Npojo10MeB7kZpuBgwksJyKmXgC6Xbt2tmMAlqSub5/CIKk5k1JuXa1SKMLg7wKrCUmGMlkBjH8BIB1U8wUuIg1SaztYNTWXwdPH1yCbfp6u8Av1gGCVXa8zOYSXIMTAZ5jehf9IYX7v//+s9dZq85JFQUwOBkhE4DRfqrhsuYmKXfffbc9eSCoIXWImQOCAz6DFGBxzT///GO6dOliU/Yig+p4OLCcTLZEcn25XRS0QkW8P1wqV65sgtJ/l5mbaLfz/Yo3nAyz36PqclLtjvgeMZjrZ0OwLrl58+Y2wyieUiVTg/XI4fsBTp55XzgupefMro+Mk9S0CkqpZR0z26RK8y/HL4JrjkHpXfhr8eLF5rHHHrPt83g9aTPnO/vss1N1nKdTAAPd1DCR+OHXH+DzSC2J8M8W5wZMRPgTJi66Pd4HeUkFF0lPr732mnfGGWd4nTt39j744AN76dSpk5cjRw5v5MiRnqvmzZvn3XLLLV716tXthevc5qLChQt7q1atSnQ7t5111lnpui2ZMmXytm/fbq+fd9553pdffpngfl7jkiVLpvr3HThwIPT7XNWhQwevZs2a3rfffuvlypXL++STT+z36vzzz/c+/vhjzzX58uXzFixY4AUJ35NffvnFXs+TJ4/3+++/2+t//PGHlzNnTs811apV8xYuXOgFxa233mr3J5MnT/a2bt1qL59++qlXtmxZe1+8WbZsmZc5c+Yk77/66qvte/TDDz+EbuP6xRdf7LVs2dJzzZgxYxJc3n33Xe+LL77wdu/eHZPtOfvss+3nIyk//vhjsu8PZs+e7f3777+Jbuc27ouFWbNmeaVLl/b69+/v/ffff17WrFm9n3/++YR/z759++wlCHbu3Gn3dYcPH/Zc1LBhQ3vh3Klu3bqhn7k0adLEu+eee7zffvvNc1Xu3Lm9FStWePFKgbWkO07+33jjjUS3v/766/Y+ib38+fN7U6ZMSXT7Z599Zu9LTxwcduzYYa8XL17c++mnnxLcv379ejsok5HwOsyfP99ez5s3b+ggOWnSJO+yyy7zXFOuXLm4PlCejDPPPDNqYP3dd9/ZgSvXfPXVV16DBg3s9v/zzz92gCr84pr9+/fbE8zs2bPbgIgL1+++++6YBAiRr2fkhYGn5AI3BmsWL16c6Pbvv//eyYGceMPgRL9+/ZId+OBYlRzev2iDurt27UoxKD+dGKxo1aqVHaw90cD6pZde8kqUKBH6DjHI/fLLL3su2rNnj3fDDTfY95Hn4u+z2U/07dvXc3EAnucUNNXifJBXgbWkO05e1qxZk+h2bmMm21WHDh3y3nzzTa9nz5728tZbb9nb4kHFihW9tWvXpvrxDz74oJ1x46DJCR2XYcOGeUWKFLH3pScOcmQAXHLJJTZAIXgMx0j/Oeec42UkvA7MfKJUqVKhzAhXZ0OnTZvmtWjRwtu8ebMXFK1bt7aZOP77tW7dOhvMXXnlld7tt9/uuYbvoX/CGe3iKoLo5cuX20ssZ9ySe225+PcnhZn28NlqH8F2mTJlPNfMmTPHDgr43nvvPa9x48Zely5d7PcoFtvDjHlS2CYyiFI7SByOgVEGSGONzACCsS1btqTq8QMHDrQD7YMHD7bHIC7PPfecve2JJ57wXNOxY0c7ME1mXvhgKMeniy66yHMdM/Bz5871/vzzT89lX8X5IK+qgjuCAjgvvvhigjXJFGSiSIRrLrzwQrtGgv7O4SheRAuNlStXGtewzfTmO3LkiK0CDipNsx6MQiDptQaJaqNJrfdkfbK/1jOlwjysw6EvNwWWWMviF/ZhXS/vG8WX0gvraMLRK5MCMD7W5m/evNmMGzfOZBS0dqP4EoXkqEtApVm+Py+//LIZP358qLejK4LYl5vPJJ9Tvivst/nc+oWKqKKbmj6x8STeeqKeKhRC9CtN+/UkYoHPBbUi/BodkSh+R42LpOonfPzxx/YcgWKMFKYC63jZZ1NU88YbbzQu4f1gfSgFAKmzwbkOa3ppTcV3iSJmrvD7jVMTg7Xa4Z8z3k/eJzqKTJ8+3bjUPow14y+99FKizxZdO7p3725bOrmEteXUKOAcLrx7CIXp+E650N0lHDUX2J+w36AWC+voqURPDYZPPvnE1l9wUebMme2/Sa2tjnWNGQXWDuCAyY6ZkzQav4ODC1WbKTh13XXXGZewzfR9JhANfz4zZsywQYFrJwBo1KiRLQ5Dr1B6JvttoTp06GCLyqRXf3F2OFT6zJo1YV1C+rNy0CBgYWd0IlWWaacBAh6JD1SQpaAbB07aOXGA/Ouvv+wBc8yYMbYFmUuC2peb9yi8UBEnbLEoVCTR92vsn2m5xT7Rr9rOSSj78kGDBqXr9lx++eX2e+wXHorEZ4jPT3jF/MgCXzwnBqf8wI2BXvb57LtdG5yiZRMVp8uUKWMDbPZzFD2lpzjnPFu2bDGu8PuNs5/jXM4/RwD7bIocUmCTAVKX2ocxcUCgFt73HQwgMhDCOZBrnzkmsRjkCA+sKRp45ZVX2mOsS5hIoZo9+w2OQxRvpagu5wich1PAzkWz43yQV1XBHcCIIQd5ZhzDPfPMM6Zv377OBdatW7e2B5Jhw4aFKlFXrFjRVpxlJs5FbDs73/ADJtd57y655JJ02w5OCqn4yMw/mQE+Tq4YiCHT4WR78MLFHrycaFL5dOjQoYn6jLuKjA8f3xkGTpjVoc1WPJ2cpZargXNKGOAikHax8nw0VMzmZCxaS7Twz6QLyPjiRPmXX35JsI++9tprbVZOegfWbdu2TTYQIcOB7hPhaJUUVByz/Ndj5syZ9vUBgx7xWLU9OQy4g/Me2gTRzz7WTkX7MDoE0PWENmKRmXMudg+gZRjdRfzvmV8Fnu9Zw4YNjYv7a/98gMxJOlLw/WHgPb33b6cSg20lS5ZMNGPNexUXWRKxzkWXjLUmmeqXrJHy14cGBcWIqK4ZiQrWrEtOTxQdo4DIiy++GLrtRAuSsMbKL2QSXsSDNaO8f64pVKhQ1O9QEGzbts07duyYFxRUZ96wYUOCi4t69+5tK7VHovOBi4VwqDbPusMsWbLY7xM1GPyLi8XYKAC4dOlSez18PSX/UnVWYl8srGnTpnatbrZs2byNGzeGjqmsJ5e08dfs+7UTol1Sqp3w9ddf25oerD+mwBcXrtOpIqX15vGIwqicr1Fhn/PuNm3a2OfD/u3XX3/1XMP35MMPP7Tr/3kOM2fOtLdTT4J9uKsyx2kRQN//n6gucY11LNFSH+iPStqvazM4pEMFbQUC6V2k5bKGiub1XFhn1LFjx3RPy2V2mVmlr776yqYXbty48aR78DLiGZ62yognM9+uYcaQ9KegYL0UM26krrEPWL9+vb2dNFLWxbuGGah27drZLA+eDyPS4RcXvffeezaVMhIZBrHow5tW1DIgI4aUdtYls8TFv7iWZuxn35DuGYl9Xjxm5LDmlZTo1KAuBvv98ItrXn31VXu+wHF0+PDhdobKn2llGZmr3nzzTVsbo0KFCjbNOPyS3mnCLDMk8yTaZenSpSn+Ds4vWEJBnQ9/X8CaeFLBY52OezKohcO2s4afzBX2ETwf0sMj091jgfPK559/3jzwwAP2X35ODuvcOfchhihatKh9v0CND5d7j3tJxA8HDhywGZUxF+vIXlL2yiuv2JlpZgrHjh1rL/fff79tMTR8+HDPNe3atbPVpoOE6t+8P5GtWx544IGYVgbns1O0aFE7y3QiM9ZB68HbtWtX2yu5Vq1atn/6ww8/nODimkceecSrXLmyN336dDs74L8/H330kVejRg3PNUHryw322dEq8XOba5lGkfuBIGjWrJk3YMCABFXbjx8/7t18883e9ddf77nWx/rvv/+2/bc5LwhS1fYgeeqpp+yxlc8d+4BevXrZWVGqaD/77LPOtQ8LAr7rfkuqd955J656Vzdq1MgbN26cvU7FfyrHMwt91VVX2RaVtHRcsmRJsr+DrgAcQ/fu3Ru6berUqbaitmse/n/na+zPiIHCz+F69Ohhe3ZziTWtsXYAVT0ZXWQdC+s//DXJXGftqGtY5ztgwAAzf/58U6NGjUTrjVKqWB2PGCVjppDKzH5F5vPPPz/ma6n47FB0g3XXjFqeyLrkaP788087S+oavxALWFMZLqnKkvFswoQJdh09xf/8CplgXdvatWuNayhcSLZHnTp1bBVt6gNQO4LiTFSdvf76641r+P7PmjXL/huOjA+yQVzDsYbMqfSeWTtdmPFp3LixrSNBBgg1TH7++Wc7M/zdd985t+aV2alff/3VVpZu0aKFPT9g5pr1oTxXF1E4i+NptDX9DRo0MK556623zKhRo+y+jfeFjDb2D1ynUFt6Z6Aww5cU6pGQFRkp8viZnBOt6RILU6dOta8DBf4oMkc2BOuQ4wF1e6jyD+o+kNVF9oZ/zsJ6fb73yRXzIkMqsnbRVVddZVy0+P8VW2PGmq474ZlFXGcWntck1lQVXNJdcqmdJ1qxOt5QbCU8sA4vZuZaajtFLyhCQroklT852BDgMMjjYiprkJCeTxDAdym8einvE8E2ratcwnPgxJKAs3Tp0rZ1Wt26dW2KOydnBw8eNK7hBJqTHgI2v/ANJ6rPPfecPZEmrdolbDeDu6RGcgIT2RItFgOipKtSODK8JSCDM9FS8KPZs2ePTTkOr9p+//332w4K6Y0BMr9YUlK4P6kqzWyzPzhFkMBJ+QUXXGBvY9D322+/NS6hUwhprNHS2JN7HeJ9v+0XmeSzSkEpPnOc8/CZdaEoW2o+py69RwziXnHFFaHWlQxI0fouGgas0hMTM+zjypcvbz8vDJrRhs7HIDqfG79zS7SuFBTO47gTbXAqvbrVnGoMgNBaNNpSnnigGWtJd3/88YcJGmZ4GVFkdJHZDw46tDzhZJNZ7MiT0HjHCTTt3ejdePjwYVvx1+/BS6snV8VLz9q04mDKSZkfzPgj2MyIcGLtGk4c+HwRWPOZIyhlYIp//d7rrqF9Dt971sX269fP3sbzY8bBtQraoPovgQGz8FzC8flLz8CatZx0l2CtIEEK6wfBbDOBGOs7yeog4yE1vaPjAZ9zBjJZ2xkN6zzJ8EoKg2n+d6VAgQL2NSKw5v+hRZVr7rvvPjtISEVwnpeLmUWRyBpjEIjPrJ/RQmDNIEg8ruvPCOdvDDoxmMZ3j89YUh0cYjFQQN/pKVOm2OMjA+crV65MEFgzGJ0/f/5kMxYZKGAwlP8vCN+hf//91w4WMjMd3vkmniiwjmORPSqj4X5OJlzFtvM8w9NZXUQhKfqhEnT6vblJJ+TLz86Y1mKunQAwixPeg5dgwNUevPHWszataLVHOteqVavsqDTBGjPYBBrR0vfiHcXyNm/ebK/T6oR+vnyX/L7cruIzx4XvD1xcRhGPJ9SdOnWyA5h8/jnpDEc6NIUkOVkmuHalfRgBMAFWUoF1SrOEQRucYhaX/XXkUgqX8d4yUEDAxD6P1F4GQ/ludevWzbiAjKIgadSokd2PgPNQCoL5A3Wx9tRTT9lZco4ft9xyi+nRo4c9d+G7zr/M2iaX+sz+jyCU2figyJYtmylUqFCi/XU8USp4HEsu3ZaRKkbYGL1Jaj1svPLXs73++ut2NpSTAYIcKhpT+ZOKh64hOBs7dqxNKYpcT3nbbbc5PfgRBMweciBi1o2etX7qNOsayTRI7/VtpwIz72RDhKex8h2qWrWqcR2p3y735U6qFzypfS72go83pAAyiMRnPhoCVNLvk1sS8cknn9jgmeU7zPqED2JzPb0rnc+dO9eu9Uyq4jX38VlKqtoyA1EMsjGowOMYnKJPtz84ld7dKdKKtFyei4v1FVKLZQxcyCzg+bqIGVWOqwwOUCuDczjO7Ti+Ut/FJRs2bLDHnHia2eXzQUC9aNGiBLczWMaEDvclNwjCe0IF+iB544037Np4Om8klbYfSwqsHcMXn7RCgjh2xIxouVAgIhyBDGmsFFShIA5BDTthWj88/fTT9qTANaylZi0MReUiC31QOMLFNaJBwvrDadOm2RPx8DXJzIowu+PPKErsBSWLhZkPv1ULo+t+lgQp0xRoc6EtGgM1ZBAwIMD15AwePDjdtovBFmZikgoyWU9Ma0CWfiRXnIkiUk8++WR8tGg5xVwfnGIml0wiCnxFW9Of3utdJTEGbJhpJ4OErDyyptjHkSXBbCmz8/GOczQCT443KRVmi+W5Nks7OF/hWMJ669S0oWR9NcsNRo8eHah93CWXXGL3bWSD8jpEFgmO9dIXpYI7ghMEDjKM1LCGksIetWvXNi4KWkVj0PeQ94cDjT8TdeTIEXsb90lsudazNpoTGZxxLV0/iFksfi94UvPDU3EJ+DgRTU/MdjIYSycG1niy3+WEhNTU5GaVSJP2M6L8iqzRpPcMD7Ov7du3tyfzbL9/YsXz5GSeonGkTqY0gMMgR5BOOCP3Aakt4haPCKjh1ydwsTBWNJs2bbLZCdGWHyQ3+xiPKMJIX272aRQB9HHOEy+1C1LCoA37RLIOuR655ML/OdafOQabI2tGsA/jmNm/f/+o/w9ZrSyNIbWd42nk4FSsA9CTRfYXl3ilwDrOMZPGTAAnEKQLseaIolIuY2cQreoqKXmuHix5f0jhY22yn55IO4CsWbPaVByJrfr169siHsy+hR8sOTHwKzbHO9bnphTAxMMJwMng5JmAiFnI8BaCnKCRxeJiYM2sKenKkeuqOcHh5Dq9sH6Q4JM0YQaRWMvObB/p0pyUkfk0fvz4qANM4ev142ntPsUVCUoIsHlefvcFjiHsc++66y773c5I7cOCVgE4ntdQniyCUAZzGPQgiyBy+YFrgTVLXJg9jMRglSudKUhh9wPWeKojkRoMCAwcODDJwDreA9CT5Z/HxSsF1nFs6NChtkgRFT7ZIdMCKQiCVtEYrGvlIMNMPCkqYBTX1WJfQRNvPWtPRjwFNqdaELNY4qUXPIMSpDyTes6+9sUXX7SDGKzdI7WQwjacnLGsyBVU9KcDAy3AGCAIb7dFETDaTaWEtM5evXrZWfx4aR+WFkGsABw0AwYMsFlsZOIE4f1hDS8tHiMLmlFbxpV1veHbnlRhti1bttiBOgb00lNae9vHewAaVFpjHcc4wWQknhlqRuGTklLl03hDGhQVjUnlY8CA6q3hFY2pmCmxRYoohWMY7WQ9aBDEU89aCXZf7njqBU+aNK8na4r9mU1eb04W2T6KwBBERusX7KqUUiSR3BpFgp6UTlrjTcGCBW3mQZAqAH/22Wd2UNSv2sxgyMMPP+xsoS9mRhcuXBiYSuespWaggEkgzudYCscEA4N0DHwl1brKNew/mRBK70ywtPa2D6qjR4/aNpbEPhy3IgexY/16aMY6jlGxNAijmtHSckmTpqIxM1KMyhHkMHPgWkVjvuBUa+Q5gefEbT6CUkanXetjzQkAB0pmhjg4EmQzq+OyeOpZe7KYnaO9xuTJkxPNyjFwwMwkJznhvS5dEMQslnjpBU9rEgrf+IH17t27E6RP0ws0vStgxzpF0sW0z5QweEOhsqCg0jSFsUjr92sSkF3EgBVLr2iV6GJvbr77fDaD0m2DQToyP6gBwnvDoCGDIUEJql3rbc/gLUXYGDRNqWWvq/v93r1723MglpCxb6DtGMurGKyOh8wrzViLpAFr2ki/o/Kif3LDTAgBKdavX28Poq6lFfrrFVnzys6KTAKCNQJsigKxPCHekQGRWg0aNDAuYE0pAxzRCvqAg8rKlSvNuHHjjEuCmsVCAMssIrPVfpZEei8PIUWY15cUcPZL7I8orMhtIC2cIMWlGdrUpEh27do15jMX6SloFYAZCKKDCMFbOAq4UnfGxSUirBunvgEBDQNakQPuDCS6isCafRyBXdDEasbaX9bB7GxS28UxJbweAedrN998s93Xp5QVxfHWRaVLl7b7Odrbcs7NRB37C74/1KEi0yWWFFhLumHkmYNkUlWyqdB84403OlVk5fLLL7cnrmw3wtNYwRedEx7/JNZVFDwi7ctvqcMIKlWP69ata+JVZLsmf+TW3+WFj+S6cgLO54oDB7Og0dC6joOxi7NxQe7LHUuk0bPvpW8zn3O6SbCe3U9Hpbgi+15Oxk4WJ3bp2R7tZFMk47V92KlAESkqAPO8g1ABmMCAwTU/08JHQM3gIlkgriGNndl2zoGo1Bw5mzhx4kTjIt6T8HT9oKS6xzqwTmtv+6DKnTu3/byRoXPOOefYcyL2f5z3kAUb6/apSgWXdJ/dpYhO5Cg0SKGmSqtLSO30q4BHQ+DpWqXPaAcV/71jHd9tt91me/QyWkhbm3hIvYkmfF0uByhOaige46cWU7yJkWCCOVewLjZa2zAfRbH8Qk6u4WSMwZug+Oqrr2wwS9YKyGShEjWDcemJzwtr0QhEmEGPLJx2ol0myFohw4PBRH43rexIAZ0+fbqJ5xTJeG4fdioErQIwATUZU8xah5s0aVKiYNsVrPvnu0idhSBgiQn7g2nTpoW6CvD9at68uZ1UcGX2OqXCwP/884+JBX+JYXIBZkYLqsHAIcdVAmuK5LFPILAmkyl//vwm1hRYS7pitoCZTtaKUkgquaJsLmC9YngaDkVzwtM8WWPt4sg6z4tAmmIkpBaTpktwTRqbPzPFLBi3x2tg7fe2BSdnDOiEt9YiMGCggM8jJwIuoOoxs1JJFV5iFNeVk5kg9+Vm4JBUNTILKlasaGdWSWsnGOR7QzprejtV6cEE5y+99JJNte7Zs6edZUuqAvrpQtDMMSSpwDqp2ex4bR92KgStAjCDoCw7YlCUAoaYN2+ezbBgeYWLqK/A/iAoCKrJZmOQyh/I4nt5zz332D7ksU7JPZlzhaTup0aGxIf27dvb9G8GeFlvTTFDzu9Y4pTeldujIhVcJD1kypTJ2759u7do0SKvZMmSXr169bxt27aF7ud65syZPZecf/753tSpU5O8f/Lkyd55553nuSZ79uxexYoVveeff96+Z9Hs2bPHa9iwoeeCHDlyeCtXrkx0+4oVK+x9rrj33nu9unXrRr3v2LFj9jvFY1zZH/B9T+7iP8YlEyZM8PLkyePNmjUr0X1ffvmlvW/SpEme69599137/uTNm9fbtWtXuv7tOXPmeF988UWS9+/fv9/79ttv03Wb5NT7/vvvvbZt23rVq1e3F64vXrzYc9Xrr7/u3XLLLd6hQ4e8IMiZM2fU94P3jftETiXOcZ599ll7DlSzZk3v0Ucf9Q4ePOitX7/e++ijj7zly5d78UBrrCXdMNNJmiozahTvaN26tV2b8/HHH9t1f8z20vrIlfWufo9YRmipaB6JmWxSwUlRYTTNJTyfeF4/fTIpVaStUpGVapnYtWuXHYUmZdyVNfCkgrPWi3VFrBH1e4UyU00lVnok83nk/nh3Iss+XEp3I4uDGbbHHnss6v1PPvmkbblDmysX8Dljlu2OO+4I3UYFVp7j//73P5uSx8xxZMqunH4ZoQJwkARtHfwFF1xgswcil12w7pf0apcKIkr8GzRokHnmmWdMu3btbFcLaoVw7Im3on8KrCUmgTUIoGmnQRVgAk/SOVwLrBkMYI11iRIl7BrecuXK2dtXr15ti98QCJGyQgqvSxo1amQHPCLXq7CekpZOLhWY89fCs90c6M8991x7GwEBKdW0bfDfNxfwHGgx4Vei97Hmffjw4c6uPwwK9mGsOUyq9sLSpUvtWlj2Da5UYOU74rdwI0AjoG7cuLFNbSc1l/04+zxJXy5XAGbgmTTipFqEsRaZ5QsUYwxHejH1WFq1amVck1KbLdfS+TlHoNsAPav9gprUZKGgK/sEv6irxBbn1CxLDMeAHIXmXBvIeeKJJ+wSEdDqlnRwOtikZ/HMFMV6ylwyDlI6o6UVjxkzxqYNtWrVyrm0T6xbt85r2rRpojRWbvv99989l9P2I+3YscPLmjWr56Ljx49706dP91566SV7mTFjhr3NVbt377bLKrhw3UU//PCDXU7AsoJI//zzj73vxx9/9FxbRrFly5Yk7//zzz+9M844w3MF+2b2cf57Uq1aNZuC52Mfp7RPibRv3z6b9pwrVy6vePHi3sCBA20qZ2qXflWoUMEunYj01VdfeZUqVTpt2y3JO+uss7zChQuHLuzLeB/ZB3DhOrdxn8SHbNmyeQ0aNLDn2nwHOQ/Knz+/55rs2bN7mzdvTnAbS/k2bdrkxRO3K0eJU5JKjmAknfYZVMx1EbOeVMT966+/Qr01mTWkMJZrGMUMn+UlXTp81JPn6UKacTSk31EB+USrIMcrPl+u9nf2kdVBdkS+fPmiFvphFv65555zqi83xb2SK8rIzEF6F/tKi/Lly5unn37a9t9+6KGHbAHD8IKFzBokNesosUFmGLO64dL7PerXr59dlsJMOlWVWQJBijAVfP0K0sklTJJRxAxVJFKoXWwnGBQvvPBCrDdBThDnbbTi5L1j371hwwbb0cU1//33X2jf4eNYG2/HU6WCS7quqWRdXlInnQRxlMtX9cXY8fvD+iJ3D6xrIW2faqCumTlzpq0ETCpreCV3xNsanYwiiH25+Q6xrIX03GioXMr6aleWvHBSRkonAwIMdNBbmBRwUsO/++47c++999rUz759+5p4R42F1HLtOETwyvtAi6rIoBrp/XljwJlOEn4nBjpNUH+AATPSudne5JZ+lSxZ0ranYiAnHN8dqk67spRC6+AlvW3cuNGeu7GMJxzdHFiyyHkcSxQ5/rokc5RjKy0tmzRpkqCyO8tIYkkz1pJuUipAxIHHtZOZoCGAYYfMDpdCKpwI+Bgp5CQhcq2OC5g9Ydbt0ksvtT1wXexTG0RB7Mud0lpW11q3NGvWzGbjgBMa2iCxRpzgjX0FRYpcKVxGe7Bw+/fvtwMd/iwIz4nnyGfSpfcIzEBRGIuBEIJR2iXy3WGWisKGsag/En5iX6hQITu4ScYQ20dtleS0adPGFgclEPfbbTGQw9pdf42lC3jt/X2cZnslPdDSkXZn4d+/9957z/Tp08eui6f1Y//+/W3xL9ePre3atTNxJ9a56CIip1uRIkW8sWPHxnozJELp0qWTbZs0bdo0r1SpUum6TZKyv//+21u4cKG3ceNGz1W0Z6ldu7Zd5+/jep06dex9rmEd8/z58+11WqD99ttv9jqt3S677LJ0357y5cvb72+0tde8xlWrVk12jfXhw4e99u3bh9bscsmSJYt3xx132PtEJDrWT69duzZB29cCBQp433zzjf156dKldq28nB5KBRcRizT8K6+80rYA4XpyItPz4l3RokXtbEe0NXsSO/fdd59N9543b16i+0jXp+In9RdGjhwZk+2T4KITwNixY03NmjUT3L548WJbafv33383LmFWlO8SXQ+YqaIuAS0TWatM9d+DBw+m6/Y8+OCDZuvWrWbixImJ7qPFIccaXuuUUtTZ/pUrV9rrlStXTpTeGu9O5HXPlSvXad0WyRgKFChguzVQg4XuIbRKZKaadm9gaQL7BpZjyKmnVHARsUjv9NuhcT0ppFG7sj7UR/ogLUGGDBkS602RMKSj0ZebPqhJ9eX+8MMPY72ZEkC0eoq2JITbCAhdQ5E5Ck4SWFOzYNSoUeb888+3/7L8JRatpZJaB80gAGnhtJ5LCc/Hb5HoIpazpLT0iPktF4+rEp8oCNq2bVvbFpHjJy2q/KAaLMNgkEpODwXWImKFF/SKLO7lOk7gOJGj8M2FF15oZ+XDxbrYRUZF8aL58+fbvtycCISjUBYnBa5WoZf4xsknhdcoXBjeg5fPIve5hhliBgv8fsjNmze3xdpYPz5mzJiYzJpxSW4ml4KmKdVecR0FM4OOwrNkeFDQMKmijZJ+KPrHQDX1cMaPH29uu+02u2+rXr26zdybMmWK+fLLL2O9mYGlVHARCTxSoZJD9VqJrSC0qxN3UFyLAmUMuOXIkcPeRiGzxo0b20I/LB9xGYHr6tWrbZstCoPGG070yVbRLK279u7da4+tVGZmxn3NmjW28CkDVmS+DRo0KNabKP8v9ZsMkhUrVtiBaoo4MvAmp4cCaxGxaKGTWp07dz6t2yIibvXndhWVtLmApQisvXYR6Z70GY9cp3vo0CG7rIJlF+kppTod69atM127ds0QgTX9vHlvJk+ebPLly5fgvj179pjrrrvODB061M74uuTuu++2wTQ1MEg1ZrCEwJr3nk4BrPkXyWgUWItIqO9oajAyzUmRi3744QebssYactoeMeLObJXfbkdEoqP1Hq1NmKFKqu+4y7PXPD/6pLqKtE/WhjNTGI7+0dyW3gEsryXHiuROMTPKumJah1GEkbaP0Tz11FO2QBsF51xbyjNt2jSbYsy6eT+w5vyAfQTt7EQyGnePIiJyyntYp+biYlDNCScj6nXq1LFreTmRRq9evRL1thWRxJhR4+Sf9F0ur776aqi/tYvoWc13n+JSpEdSfRqsTXzllVeMa/wCWJF4z2KxrIKCaVQipl5HtEtqCpcRqHXp0sWmrfoF5T766CM7QOoSqp9fe+21Sd5/zTXXmIULFxrXMDDt9+gO9/fff2uwWjIsBdYiEngU9qGyLAf88FTJVq1aqYiHSCowW816ZAbXbrjhBvPyyy/bGaubbrrJfPHFF84VPGT2kOdDoBZecOnSSy8177zzjnEFM+3MSBNU01aL6/6lUKFCds0471F6o9I/KdBJSWk2m3Ri3gtm3CkARko7Nm3aZNPeXUJ19GgBqI/BHTpyuKZ+/frmgw8+SPSevvDCC6Zhw4Yx3TaRWAnegikROSVYd8hJ58aNG+3sTjgq6brk22+/NXPmzLEnMOFIW+NETURSp2TJkqZv3772QnDNDC/7CWYoqb3ALLALlYHpBPD++++bevXqJUgBpw2NX0TPBQQxBDN33nmnHSw488wzQ/cxa8iAIpk66e3hhx82Bw4cSPJ+ChQmVzGbNeFkDnTs2DFBUEpv++eee864pFixYvZ4mtRyK9oLRqbwu4C1+wzckEHAOULv3r3Nzz//bDPCqD4tkhEpsBaRRD777DPTunVrU7duXTNv3jzbD5GTTVK/mjRpYlzz77//Rr2dPsmRwbaIJI0ZRAJSKukTLJDiyrprZuUI8jihTqlwVTzg5J8Z90jMjLq07rd9+/b2X4I2BgnipcAcs5nJocZFcq22CDZpuRcpf/78NvPIJc2aNbMVsvk3EpkeTz/9tJNVmlk3Tu90loXkzJnTnh+Q1n7//fdH/W5J7AShjoQr9AqLSCL0QX3mmWfsjAKzT2+++aYt+sVB07XKpWAwIHzdJClrzKbQgsLFExqRWAy2kQLOeuTRo0fbYJq+yfQaJ2BgxpTHzJo1y7iAdeKksPv89clk48RihvdUzIqSpu9j381s74svvuhcmj6YwY1Wz4O+96kttBkvmH3n+El6PN8X1o5zoccwtT8YtE6qsFm8YR9AAA36pFP8s0+fPjYDhAE1CrEpqI4PQasj4QyqgouIhMuVK5e3bt06e71gwYLeihUr7PWVK1d6Z599tueaTZs2eZUqVfIqV67sZc2a1fvf//7nFSlSxLvgggu8rVu3xnrzROLemWee6XXq1MlbvHhxko85ePCgN2DAAM8Fc+bM8fLmzet16dLFy5Ejh9ezZ0+vWbNmdt+3aNEizzW1a9f2xo0bZ69v2LDBPo/mzZt755xzjvfoo496rnnqqae8iy66yPvhhx/s+zR//nxv/PjxXtGiRb2hQ4d6rvn999+9Jk2aeJkzZ05w4bY1a9Z4rsiWLZu3ZcsWe53t3759e6w3SZLwyCOP2HOe6dOn2/0Bn0F89NFHXo0aNWK9eYEVHzlDIhJXChQoEGqVwUjnL7/8Ytcectu+ffuMa0qUKBGaIVixYoV9Hrfffru59dZbE/V9FZHEPaxJ9yTzI7m1oKSDku3iAlKVf/zxR/Pss8/afRuzbbQNYka0atWqxjWkTtesWdNeZ/awdu3a9jnNnj3b7ut4ni5hvS5rx0kXP3jwoE1zZ8149+7dTbdu3YxrqOcxY8YMW0nfX8PPOvNYVGxPiwsuuMB+ltgX8P589dVXCdb1h2vRokW6b58Er46Ea9THWkQSueWWW+yJGScwVGAdMWKEuf766+2JAangtFERkYyDoHn16tWmdOnSsd4UiSJfvny2hRXBGkEPS1wIQik+Wb58+VBVbRfTWQkCGAyl6rlqYsTW119/bddQ856wxCCpECKj9CiPZ0waUEyOpRPhfcaZXCDYdnGSxAWasRaRRFh/45+IUf2XmQJmcgiu+dmlk7JFixaFCukw0h5e4TxLlix2vVG2bNliuJUi8Y+1oJyQBSWwZjaH9aHUjQjHOnH2EbTicwnrdymQRcEv1leTYYANGzaYokWLGldx7CGglvjQqFEjmx0BZkHpL+7y5yvI/DoSdGsIQh0JVyiwFpFEzjrrrNB1Dp69evUyLqLHJhe/oBKFVRi99dsBUcyDtHf/wCMi0T3wwAOmR48etpI+QRxVncO5FvyQsk67sEjMiPJcXQusX3rpJbu05dNPP7Vp1OXKlbO3T5w40XZ3cMGJ9NtmYERii2J5LrYJyygoQHvVVVfZgRCW8wwfPtzOYNN6NLlWd5I2SgUXkURIH0xOqVKljAsuv/xy06VLF3PjjTfan8PTofyRW9oGzZ07N8ZbKhLforVpYQaEUwgX0z5JbeeEkz7P4RhsY5CAdb1BcPjwYZuZ40JWDpXmU4v9tqQ/6q1UqFDB7g+4nhzXBtuCiGr0ZOpx3sNyCupIkKXnYh0JV2jGWkQS4WTTTxuKxpWTaHpsciBJCjM5zMKJSPLCWzkFJSuHwCAysF65cqXtlRwUpLu7QsGyG72rt23bZmeque4PrgVhsC2Izj//fDNq1KhYb0aGosBaRBL56aefEvz877//mmXLlpkXXnjBruNzxe7duxP0cN2+fXuCKuDM5DCjIyLJY60uA1FZsyY8bSDFkPoLrq29btOmjU35pqIxhXzw3Xff2YKNFG90DTOIQRgMjUSRLH9NLzOgBAoS2wG2woULh65L/ApaHQlXKLAWkUQuvPDCRLdRDfzss8+2VcIpYuZKm61ff/3VVspFZGstTthoJyYiKS+roFBR5JrKPXv22PtcC9yot7Br1y7ToEGDUJo0gwS0pnr66aeNa6ZMmRJ1MJRZYFdaoIXbuXOnufPOO820adNsATP/OVHtnCU8WtsbG+EDaEkNpm3ZssUOwg8ZMiQdt0yCXkfCFVpjLSKpRpBKpckDBw4YF3DwWLJkiZ1Ri8RMNjNwVDumCrqIJD8jSsaHP1vlW7NmjS1mtnfvXuMiZt0o6OP3d3Vt5j0lkyZNsoEoPa1d0rJlS7N582bz5ptv2s8X2Jffc889djCUWTeJT6zn5TzBtcG2oMkodSTijWasRSSRyKIkjL+xruq5556zJ5+uoDUYa6xr1aplHn744VClXPrxDh482I6sU0VXRJKv1EyacceOHUMV9cGJMyfRrlSdjoYuAVyCikyj2bNnG9d89dVXtnqxH1SD6yNHjjSXXXZZTLdNxAUZpY5EvFFgLSKJRCtKAgLU0aNHG1fQX3PevHmmU6dO5uabb05w35VXXmlb0RQrVixm2ycS7/y2WuwLmAHh4iNF96677jJ33323cQ1pxslhltclkbNP/mAoS3f8LgguYVY62ppxbtM+WyTj1ZFwhQJrEUkksigJaaCkgLpUYdbHbNT06dPNX3/9ZQvhgDXXBQsWjPWmiThTqZlZj4ceeihR/2pX7du3L8HPrN9lJod1102aNDGuYd1kZCBKcE2dibFjxxrXkB314IMPmtdee81UqVLF3kZ2RPfu3c3zzz8f680TiXtBqyPhCq2xFhERkRMuLjVjxgzb07ZmzZomCDgdYjaHwlh9+vQxLolM9/YHQxlEjKzkHq/Y3vDBAdbtM+DhLz84cuSIDRDy5ctnduzYEcMtzdj85SFJ+eeff2wqv9ZYxwfWVDNoGNQ6EvFGgbWIJMCJzNChQ8348eNtH2hOZGhxcuutt5ouXbqERj5FJOOgGjMXZhGZ7aX4zf79+20hQwpMMQsSBBRjI21SgVv6e+edd1L92Pbt25/WbZGk3XHHHal6nPqSS0akwFpEEqzTa9y4sVmxYoUNpDl5BgUwPvjgA1vpk5FoquguWLDAdO7cOdabLCLpNJv4zTff2PoLnDDTTod2TgzAkbbrz4i47sMPPzRdu3a165NdwOAnM4TUv/DNnDnTpoEy6HHdddc5N/suIqeuk8tHH31kNm7caHtXu1xHwhVu5AeJSLrgZIz0O9YiFy9ePMF9AwYMsAW/mClgzTIn1iKSMTA7TQouSAG/4YYbbPYKFZrvu+8+45rIdFa/2NfChQvNo48+alxBtwO/8wHYd19zzTWmYcOGpmrVqubZZ5+1tTF69uxpXBjYzZUrV+h6cvzHiUh0tKRr3bq17dpAEdf//e9/dv/AOZ6LdSRckTnWGyAi8TVbQ2GYyKAaZ599tp2Z4jH0EqUasIhkDKzVpTXdpk2bzBdffGGaNWsWWmtN4SzXUIQt/MKgASngU6ZMMU8++aRxBb2d/fcCZBZVrFjRvkcvvfSSvZxIinUs5c2bN5SCz2eKnyMv/u0ikrzHH3/cPPPMMzbTiDoFLNn5/fff7cAbbfjk9FAquIiEMLPBiCaVZKPZvHmzrQ5MZUkRyTgmTZpk2rZtawsSMUv99ddf29s5caPfMIGcpD/an5EOXrJkSfszM9W8PwMHDrQ/r1u3zs5o79mzx7hQgI3BDYqtpdR7W72sRZLHgCFLdOiMUqhQIfPtt9/a4mUs5WPG+s8//4z1JgaSUsFFJKRAgQJmy5YtSQbW7IhZaykiGUurVq1sKuHWrVttirGPmgzXX399TLctI2N/zPpJAmsKTy5evNj06tUrdP+hQ4dshXAXhAfLCpxF0n4+xxIevy88tXIIrLktst2gnDpu7G1FJF0wiplcGiS9D5s2bZqu2yQi8aFYsWJ29jM8UGNtLy23XMB2Z8mSJVUXl/bZrAmnmGTfvn1tyiez1r6ffvrJdnVwzdy5c+0gge/99983V1xxhXnggQdsUTYRSV79+vVtsVl/YJSijJ06dTK33HKL/S7J6aFUcBEJ2bBhg7nkkktsn8OHHnoodMLMSOeLL75o11dyslOqVKlYb6qIpKPLL788QY/hSH5qeDybNm1aguwbAtE2bdqYOnXq2NsITidMmGAGDRpk60i4gDXJZAyw7aR+UrGdk2gfJ9AMfjAo6hLWgD7xxBN2Pejq1avtz3feeactwnTppZea119/PdabKBLXdu3aZTNWyGY5fvy4GTx4sJk/f7654IIL7L6PGW059RRYi0iiPq70q541a1aC26kI/uqrr9oiRiKSsVB9Ohxpx8uXLzc//vijDXiGDBliXELBL2ZuIvshU+iLAmBffvmlcQkttyjqFTnb/tdff9mCX9mzZzcuYZuZbWd9KAH2Dz/8YKscf//997aFGEuWRETijQJrEYmKEzIKmYFgumDBgrHeJBGJM8yEbt++3VafdgntmhgYYPYmcmCRNeQptXuS04vZNGanK1WqZFNaKZxHGuv69evtbXp/RFJGTQwGCqkGTibOWWedZZdZ0OXFxSUiLtAaaxGJikCaFEIuCqpFJBpSqV1p5xSOAo1vvfVWotu5LanijZJ+CKZ79Ohhg4FFixaZq6++OjTwQSEmEUke3xuW83300Udm9OjRtn81qA7ep0+fWG9eYKkquIiIiJyUqVOnOtlXmNR11iJPnz7d1K5d295GmjH1JCZOnBjrzcvwWHbUuXNn2+Zt+PDhoXZin3/+eYK+3SISHXVyKGz42GOPJdhHU/Bw1KhRMd22IFMquIiIiCSLoobhxcs4ddi2bZtNNXz55ZdtEORiscaRI0eaX3/91f7M7M69995rizeKiLiMYHrFihW2TgHXWfpy3nnn2eUU7OsOHz4c600MJM1Yi4iISLL8VNzw1lX0UKbfcMWKFY2LCKCfeeaZWG+GJOHYsWN2bSiVz6lqHK5BgwYx2y4RF+TLl8/WvyCwDkeAzRprOT00Yy0iIiIZDieYpESuW7fOrkEsXry4XY9IwF2zZs1Yb16GRuGyW2+91WzcuDHRfWROEHSLSNJ69uxpq+mznIJCZcuWLTM7d+407dq1sx0RqLYvp55mrEVERCSRaEFNUlzrbc9a3RtvvNG2bqIHN/1esWnTJluMjdZOEjv33XefqVevnpk5c6Yd8Eiuh7qIRO/YQOtUiv39999/tpo+bRJvuukm079//1hvXmBpxlpEREQSId07tQGNazOIzEgTvHXs2DHB+sOlS5eaq666yq4dl9jJnTu3XR+qlkAiacNgIT3h9+/fb6pVq2bKlSsX600KNM1Yi4iISCKcjPmolv3www+bBx54wNSpU8fetmDBAlu9+bnnnjOuWbVqlbniiisS3Z4/f37z999/x2Sb5P80atRIgbXISaAewfPPP2+zbo4ePWoaN25sHn/8cZMzZ85Yb1qGoMBaREREErnwwgtD1++++25b/fuaa64J3Va3bl1TtmxZWwCM9EKXFClSxK6tPvfccxPcPn/+/ETFfiT9XX/99baPNQM6F110kcmWLVuC+1u0aBGzbROJZ0899ZTdJ7OWmmCaOhIUAHzrrbdivWkZglLBRUREJFmcoP3444+2TUu41atXm+rVq4fWKLu0/nDcuHFmzJgx5vLLLzczZsywa8q7du1qevXqZbp16xbrTTQZfRlCUlS8TCRpF1xwgS1MRoEyLFq0yFbRZx+d3PdKTg0F1iIiIpIsgmfW573xxhuh2UMK4dxzzz222ixBt0s49SG4Zmbn4MGD9rbs2bOb7t27qwWXiDjrjDPOsNk4FC0LHxhds2aNKVGiREy3LSNQYC0iIiLJWrhwoWnZsqXJkiWLDbBBQM3M4ZQpU8yll15qXMQaxLVr19rCPlTNzZMnj53Z0XpEEXER++ht27aZwoULh26jQCM1C7TM5fRTYC0iIiIpIvj84IMPzK+//mp/Ji28bdu2NhgNgiNHjpjhw4ebwYMH2xNTiS2KL1GEiUJzYOCDAnoM8IhIdKR78x1h5tr3ySefmCZNmthq+74JEybEaAuDTYG1iIiIZAgEzwMGDLD9kUlp79mzp2nVqpV5++23TZ8+fextVD5/6KGHYr2pGdrIkSPtOve77rrL9rPGd999Z9+nYcOGmXvvvTfWmygSl+64445UPY7vkpx6CqxFREQkRcxUz54921aYpaVLuP79+xsXPProo2bs2LGmTZs2Zvr06bb4WocOHcz3339vA2uCbFIpJbaoNs97RTX6cKzxJ6OA9H0RkXijwFpERESSNWLECDuTW6xYMVO8eHFbmdnHdQJTF5x33nk2OKOHNQV+COAIrEePHp3gOUlskcb6888/2/cnHAE17bcOHz4cs20TEUmK+liLiIhIsqiUzXpXegu7bPPmzXatrh9k58iRw1YCV1AdXwioP/roIztrHW7SpEmJgm0RkXihwFpERERSLFx27bXXGtdRxdxvFwbSvoNSfC1IWAdPH965c+eG1ljPmzfP9hsfP358rDdPRCQqpYKLiIhIspipJg38kUceMUGqmButWi5UMTf2Fi9ebAuV+VXBK1asaLMLatasGetNExGJSoG1iIiIpBhYU0W2Ro0apnLlyglmfUFBKReoYq6IiJwuCqxFREQkWZdffnmS97E++euvv07X7ZHgO3TokO2bHt7HmvTwXLlyxXrTRESiUmAtIiIiInGVBk7KPm3dqlWrZm9btmyZHcSZOnWqueSSS2K9iSIiiSiwFhEREZG4Ub16ddtW68033wyth6fF1j333GN++ukn8+OPP8Z6E0VEElFgLSIiIlHddNNNqXqcin3JqZQzZ04bPFeoUCHB7atXr7ZBN2niIiLxRu22REREJKrIatki6YH077Vr1yYKrNesWWOqVKkSs+0SEUmOZqxFREREJG6QAdGrVy/TrVs3U6tWLXvbokWLzCuvvGKefvppmybuo6iZiEg8UGAtIiIiInHVbzw5FDHj9JV/jx07lm7bJSKSHKWCi4iIiEjc+OOPP2K9CSIiJ0wz1iIiIiIiIiJpkHyujYiIiIhIOvroo4/MrFmzQj8/88wzpmzZsra39bZt22K6bSIiSVFgLSIiIiJxo1+/fub48eP2+g8//GCeeOIJ28P64MGDpmvXrrHePBGRqJQKLiIiIiJxI1euXGbVqlWmdOnSpnfv3mbDhg1m7NixZsWKFeaKK64wO3bsiPUmiogkohlrEREREYmr/ul//fWXvT5jxgzTpEkTez1PnjzmwIEDMd46EZHoVBVcREREROLGlVdeaTp27GiqV69uVq9eba666ip7+88//2zOPffcWG+eiEhUmrEWERERkbgxYsQIU6dOHbNz504zceJEU7hwYXv7kiVLTNu2bWO9eSIiUWmNtYiIiIiIiEgaaMZaREREROLK8uXLTZcuXUzz5s3N1q1bQ224qBIuIhKPFFiLiIiISNz4/PPPzaWXXmp2795tvvnmG3Po0CF7+6ZNm2zrLRGReKTAWkRERETiRv/+/c0rr7xixo0bZ7Jlyxa6vUGDBmbx4sUx3TYRkaQosBYRERGRuEEPa/pVR8qfP7/5+++/Y7JNIiIpUWAtIiIiInGjSJEiZt26dYlunz9/vilTpkxMtklEJCUKrEVEREQkbtx9992ma9eutr1WpkyZzPbt282HH35oHnroIXPvvffGevNERKLKGv1mEREREZH017t3b0M32Msuu8wcPHjQ1KtXz2TPnt10797ddOvWLdabJyISlfpYi4iIiEjcOXr0qFm7dq3Zv3+/qVSpksmTJ4+tEJ4zZ85Yb5qISCJKBRcRERGRuMMsNQF1rVq1bHXwIUOGaI21iMQtBdYiIiIiEnNHjhyxaeA1a9Y0derUMZMmTbK3v/322zagfumll+w6axGReKRUcBERERGJuUcffdSMHTvWtGnTxkyfPt2sXr3adOjQwXz//femT58+plWrViZLliyx3kwRkahUvExEREREYm7ixIl2dpoe1p07dzZly5Y1x48fN8uXL7fVwUVE4plmrEVEREQkLtZUr1+/3px99tn251y5cplFixaZypUrx3rTRERSpDXWIiIiIhJzx44ds0XKfKR9UwlcRMQFSgUXERERkZgjibJjx47mjDPOsD8fPnzYdOnSxeTOnTvB4yZMmBCjLRQRSZoCaxERERGJufbt2yf4uV27djHbFhGRE6U11iIiIiIiIiJpoDXWIiIiIiIiImmgwFpEREREREQkDRRYi4iIiIiIiKSBAmsRERERERGRNFBgLSIiIiIiIpIGCqxFRERERERE0kCBtYiIiIiIiEgaKLAWERERERERSQMF1iIiIiIiIiJpoMBaRERERERExJy8/w93sa1W5arhggAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 990x462 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 4.2))\n",
    "g = m.assign(goal=m[\"sdg_goal\"].astype(int)).groupby([\"goal\", \"confidence_level\"]).size().unstack(fill_value=0).reindex(range(1, 18), fill_value=0)\n",
    "g[[c for c in [\"strong\", \"weak\", \"uncertain\"] if c in g]].rename(columns={\"uncertain\": \"open\"}).plot.bar(stacked=True, ax=ax, color=[\"#1f3a93\", \"#8fa7e8\", \"#f2c14e\"], width=0.8)\n",
    "ax.set_xticklabels([f\"{i}\\n{GOALS[i-1]}\" for i in range(1, 18)], rotation=90); ax.set_xlabel(\"\"); ax.set_ylabel(\"links\")\n",
    "ax.set_title(\"SDG links by goal and confidence level\"); fig.tight_layout(); fig.savefig(FIG / \"01_goals_by_confidence.png\"); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d5fd5fc5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T11:42:22.505679Z",
     "iopub.status.busy": "2026-10-23T11:42:22.505605Z",
     "iopub.status.idle": "2026-10-23T11:42:22.678916Z",
     "shell.execute_reply": "2026-10-23T11:42:22.678622Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 990x264 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 990x352 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = []\n",
    "for _, r in p.iterrows():\n",
    "    for g in str(r[\"assigned_goals\"]).split(\"; \"):\n",
    "        if g and g != \"nan\": rows.append({\"programme\": r[\"programme\"], \"field\": r[\"scientific_field\"] if pd.notna(r[\"scientific_field\"]) and r[\"scientific_field\"] else \"unclassified\", \"goal\": int(g)})\n",
    "e = pd.DataFrame(rows)\n",
    "share = e.groupby([\"programme\", \"goal\"]).size().unstack(fill_value=0).reindex(columns=range(1, 18), fill_value=0)\n",
    "share = share.div(p.groupby(\"programme\").size(), axis=0).loc[share.index]\n",
    "fig, ax = plt.subplots(figsize=(9, 2.4))\n",
    "im = ax.imshow(share.values, cmap=\"Blues\", aspect=\"auto\"); ax.set_yticks(range(len(share))); ax.set_yticklabels(share.index)\n",
    "ax.set_xticks(range(17)); ax.set_xticklabels(range(1, 18)); ax.set_title(\"Share of projects linked to each SDG, by programme\"); fig.colorbar(im, ax=ax)\n",
    "fig.tight_layout(); fig.savefig(FIG / \"02_goals_by_programme.png\"); plt.show()\n",
    "f = e.groupby([\"field\", \"goal\"]).size().unstack(fill_value=0).reindex(columns=range(1, 18), fill_value=0)\n",
    "fig, ax = plt.subplots(figsize=(9, 3.2))\n",
    "im = ax.imshow(f.values, cmap=\"Blues\", aspect=\"auto\"); ax.set_yticks(range(len(f))); ax.set_yticklabels(f.index)\n",
    "ax.set_xticks(range(17)); ax.set_xticklabels(range(1, 18)); ax.set_title(\"Projects linked to each SDG, by EuroSciVoc field\"); fig.colorbar(im, ax=ax)\n",
    "fig.tight_layout(); fig.savefig(FIG / \"03_goals_by_field.png\"); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7d3c90b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T11:42:22.680000Z",
     "iopub.status.busy": "2026-10-23T11:42:22.679931Z",
     "iopub.status.idle": "2026-10-23T11:42:22.823493Z",
     "shell.execute_reply": "2026-10-23T11:42:22.823269Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x374 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 3.4))\n",
    "top = p[\"coordinator_country\"].value_counts().head(12).index\n",
    "c = p[p[\"coordinator_country\"].isin(top)].groupby(\"coordinator_country\")[\"outcome\"].value_counts().unstack(fill_value=0).loc[top]\n",
    "c.rename(columns={\"uncertain only\": \"open only\"}).plot.barh(stacked=True, ax=axes[0], width=0.8); axes[0].set_title(\"Outcome by coordinator country (top 12)\"); axes[0].set_xlabel(\"projects\")\n",
    "d = p.dropna(subset=[\"duration_months\"]).copy()\n",
    "d[\"n_goals\"] = d[\"assigned_goals\"].fillna(\"\").apply(lambda s: len([x for x in str(s).split(\"; \") if x and x != \"nan\"]))\n",
    "d[\"duration\"] = pd.cut(d[\"duration_months\"].astype(float), [0, 24, 36, 48, 60, 200], labels=[\"≤24\", \"25–36\", \"37–48\", \"49–60\", \">60\"])\n",
    "d.groupby(\"duration\", observed=False)[\"n_goals\"].mean().plot.bar(ax=axes[1], color=\"#1f3a93\")\n",
    "axes[1].set_title(\"Mean SDGs per project by duration (months)\"); axes[1].set_ylabel(\"goals\"); fig.tight_layout(); fig.savefig(FIG / \"04_country_duration.png\"); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cea69bb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T11:42:22.824634Z",
     "iopub.status.busy": "2026-10-23T11:42:22.824566Z",
     "iopub.status.idle": "2026-10-23T11:42:22.914571Z",
     "shell.execute_reply": "2026-10-23T11:42:22.914359Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x374 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 3.4))\n",
    "p[\"outcome\"].replace({\"uncertain only\": \"open only\"}).value_counts().plot.pie(ax=axes[0], autopct=\"%1.0f%%\", ylabel=\"\"); axes[0].set_title(\"Project outcome\")\n",
    "u = m.assign(goal=m[\"sdg_goal\"].astype(int)).groupby(\"goal\")[\"confidence_level\"].apply(lambda s: (s != \"strong\").mean()).reindex(range(1, 18))\n",
    "u.plot.bar(ax=axes[1], color=\"#f2c14e\"); axes[1].set_title(\"Where the mapping is less certain (share weak or open)\"); axes[1].set_ylim(0, 1)\n",
    "fig.tight_layout(); fig.savefig(FIG / \"05_uncertainty.png\"); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "623e1a39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T11:42:22.915612Z",
     "iopub.status.busy": "2026-10-23T11:42:22.915544Z",
     "iopub.status.idle": "2026-10-23T11:42:23.005295Z",
     "shell.execute_reply": "2026-10-23T11:42:23.005055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 990x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EU contribution in the collection: EUR 965.6 million; in projects with at least one justified SDG: EUR 856.9 million\n"
     ]
    }
   ],
   "source": [
    "fund = []\n",
    "for _, r in p.iterrows():\n",
    "    for g in [g for g in str(r[\"assigned_goals\"]).split(\"; \") if g and g != \"nan\"]:\n",
    "        fund.append({\"goal\": int(g), \"eur\": float(r[\"eu_contribution_eur\"] or 0)})\n",
    "f = pd.DataFrame(fund).groupby(\"goal\")[\"eur\"].sum().reindex(range(1, 18), fill_value=0) / 1e6\n",
    "fig, ax = plt.subplots(figsize=(9, 3.6))\n",
    "f.plot.bar(ax=ax, color=\"#1f3a93\"); ax.set_ylabel(\"EU contribution (EUR million)\")\n",
    "ax.set_xticklabels([f\"{i}\\n{GOALS[i-1]}\" for i in range(1, 18)], rotation=90); ax.set_xlabel(\"\")\n",
    "ax.set_title(\"EU contribution of projects linked to each SDG (a project linked to several goals counts in each)\")\n",
    "fig.tight_layout(); fig.savefig(FIG / \"06_funding_by_goal.png\"); plt.show()\n",
    "total = p[\"eu_contribution_eur\"].astype(float).sum() / 1e6\n",
    "linked = p[p[\"assigned_goals\"].fillna(\"\").astype(str).str.len() > 0][\"eu_contribution_eur\"].astype(float).sum() / 1e6\n",
    "print(f\"EU contribution in the collection: EUR {total:,.1f} million; in projects with at least one justified SDG: EUR {linked:,.1f} million\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61b049a",
   "metadata": {},
   "source": [
    "## 6. Validation\n",
    "\n",
    "There is no official gold standard, so one was built following the organisers' guidance (Q&A 14–15).\n",
    "\n",
    "* **Held-out evaluation (batch C).** After the method was frozen (contract 2.4.9, rubric set 2.4.8, code committed), 20 projects were drawn by a fixed seed from outside the map, the development set and every earlier reference. A second AI model (Astra 6) annotated all 17 goals for each project **blind**, without our outputs or rubrics; its annotations were locked before our run was scored, once. On 20 untouched projects drawn after the method was frozen (batch C, blind reviewer), 97% (33/34; 95% CI 85–100%) of supported links are on a goal the reviewer also found, 79% (27/34; 95% CI 63–90%) on the exact concept, and the method finds 43% (23/54; 95% CI 30–56%) of the goals the reviewer found.\n",
    "* **Independent review of the published map (review F).** An independent reviewer checked a seeded random sample of 80 published supported links: 79% (61/77; 95% CI 69–87%) are correct before the author's adjudication; every quoted passage was verified (80/80), and the errors are in SDG interpretation (16/80), not in evidence.\n",
    "* **History (batches A and B).** 40 earlier protected projects measured the method while it was being built; the author adjudicated every disagreement in batch A. Their after-hardening figures are **not** held out: rubric set 2.4.6 encodes the author's rulings on batch A, and batch B's scores informed which paths count in goal navigation. The dated record is in `trace/CHRONOLOGY.md`, recomputed from git.\n",
    "* **Metrics.** Same-goal precision credits a link when the reviewer found that goal; exact-concept precision requires the same goal or target; recall is measured over the reviewer's full 17-goal review, so goals our candidate step never proposed count as misses. Intervals are 95% Wilson intervals over pairs, which are not independent within a project, so they are optimistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b309d600",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T11:42:23.006402Z",
     "iopub.status.busy": "2026-10-23T11:42:23.006320Z",
     "iopub.status.idle": "2026-10-23T11:42:23.050301Z",
     "shell.execute_reply": "2026-10-23T11:42:23.050100Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batch</th>\n",
       "      <th>projects</th>\n",
       "      <th>stage</th>\n",
       "      <th>untouched held out</th>\n",
       "      <th>adjudicated</th>\n",
       "      <th>precision, same goal</th>\n",
       "      <th>precision, exact concept</th>\n",
       "      <th>recall, all 17 goals</th>\n",
       "      <th>κ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>30</td>\n",
       "      <td>before</td>\n",
       "      <td>no (development)</td>\n",
       "      <td>yes</td>\n",
       "      <td>76% (34/45)</td>\n",
       "      <td>67% (29/43)</td>\n",
       "      <td>32% (22/69)</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>30</td>\n",
       "      <td>after</td>\n",
       "      <td>no (development)</td>\n",
       "      <td>yes</td>\n",
       "      <td>88% (28/32)</td>\n",
       "      <td>80% (24/30)</td>\n",
       "      <td>26% (18/69)</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "      <td>before</td>\n",
       "      <td>no (development)</td>\n",
       "      <td>no</td>\n",
       "      <td>61% (11/18)</td>\n",
       "      <td>39% (7/18)</td>\n",
       "      <td>47% (8/17)</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "      <td>after</td>\n",
       "      <td>no (development)</td>\n",
       "      <td>no</td>\n",
       "      <td>83% (10/12)</td>\n",
       "      <td>50% (6/12)</td>\n",
       "      <td>41% (7/17)</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>frozen</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>97% (33/34)</td>\n",
       "      <td>79% (27/34)</td>\n",
       "      <td>43% (23/54)</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  batch  projects   stage untouched held out adjudicated precision, same goal  \\\n",
       "0     A        30  before   no (development)         yes          76% (34/45)   \n",
       "1     A        30   after   no (development)         yes          88% (28/32)   \n",
       "2     B        10  before   no (development)          no          61% (11/18)   \n",
       "3     B        10   after   no (development)          no          83% (10/12)   \n",
       "4     C        20  frozen                yes          no          97% (33/34)   \n",
       "\n",
       "  precision, exact concept recall, all 17 goals     κ  \n",
       "0              67% (29/43)          32% (22/69)  0.64  \n",
       "1              80% (24/30)          26% (18/69)  0.66  \n",
       "2               39% (7/18)           47% (8/17)  0.39  \n",
       "3               50% (6/12)           41% (7/17)  0.49  \n",
       "4              79% (27/34)          43% (23/54)  0.59  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review F: 80 seeded random supported links, reviewer only (before adjudication): 61 correct, 16 incorrect, 3 uncertain\n"
     ]
    },
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       "      <td>80</td>\n",
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       "      <td>77</td>\n",
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      "text/plain": [
       "           evidence  ownership  sdg_interpretation  claim_stage\n",
       "correct          80         77                  62           77\n",
       "unclear           0          3                   2            3\n",
       "incorrect         0          0                  16            0"
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",
      "text/plain": [
       "<Figure size 715x286 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = []\n",
    "for b in snap[\"validation\"][\"batches\"]:\n",
    "    def f(x): return f\"{round(x['hits'] / x['n'] * 100)}% ({x['hits']}/{x['n']})\" if x and x.get(\"n\") else \"\"\n",
    "    rows.append({\"batch\": b[\"batch\"], \"projects\": b[\"projects\"], \"stage\": b[\"stage\"], \"untouched held out\": \"yes\" if b[\"batch\"] == \"C\" else \"no (development)\",\n",
    "                 \"adjudicated\": \"yes\" if b[\"adjudicated\"] else \"no\", \"precision, same goal\": f(b[\"precisionGoalLevel\"]),\n",
    "                 \"precision, exact concept\": f(b[\"precisionExact\"]), \"recall, all 17 goals\": f(b[\"recallAllGoals\"]), \"κ\": b[\"kappa\"]})\n",
    "display(pd.DataFrame(rows))\n",
    "review = input_file(\"review-f-score.json\")\n",
    "if review.exists():\n",
    "    r = json.load(open(review))\n",
    "    print(f\"Review F: {r['items']} seeded random supported links, reviewer only (before adjudication): \"\n",
    "          f\"{r['reviewer_only']['correct']} correct, {r['reviewer_only']['incorrect']} incorrect, {r['reviewer_only']['uncertain']} uncertain\")\n",
    "    display(pd.DataFrame(r[\"criteria\"]).fillna(0).astype(int))\n",
    "c = next((b for b in snap[\"validation\"][\"batches\"] if b[\"batch\"] == \"C\"), None)\n",
    "if c:\n",
    "    labels, vals, lo, hi = [], [], [], []\n",
    "    for name, key in [(\"same-goal precision\", \"precisionGoalLevel\"), (\"exact-concept precision\", \"precisionExact\"), (\"recall, all goals\", \"recallAllGoals\")]:\n",
    "        x = c[key]; labels.append(name); vals.append(x[\"hits\"] / x[\"n\"]); lo.append(x[\"hits\"] / x[\"n\"] - x[\"ci95\"][0]); hi.append(x[\"ci95\"][1] - x[\"hits\"] / x[\"n\"])\n",
    "    fig, ax = plt.subplots(figsize=(6.5, 2.6))\n",
    "    ax.barh(labels, vals, xerr=[lo, hi], color=\"#1f3a93\", capsize=4); ax.set_xlim(0, 1)\n",
    "    ax.set_title(\"Held-out batch C (20 untouched projects, blind reviewer), with 95% intervals\")\n",
    "    fig.tight_layout(); fig.savefig(FIG / \"07_validation_heldout.png\"); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f744ec5",
   "metadata": {},
   "source": [
    "## 7. Limitations and uncertain cases\n",
    "\n",
    "* **Recall is the weakest point.** The method finds well under half of the goals an independent reviewer finds for a project (batch C: 43% (23/54; 95% CI 30–56%)). Most misses are goals the candidate step never proposed, not wrong judgments. Precision is high because the method prefers *no justified mapping* to a guess.\n",
    "* **SDG interpretation is where errors remain.** In the review of published links, quotations and ownership were almost always right; the errors are readings of what counts as a contribution: a real activity credited to a goal whose mechanism the text does not state (in the review, doctoral entrepreneurship training read as support for enterprises under 8.3, or industrial energy efficiency read as access to energy under 7.1).\n",
    "* **Not assessed ≠ no.** 1,475 candidate pairs were not yet assessed and 77 could not be decided; they are published with their cause and never counted against a goal. 5 supported links from the first run are held out of goal navigation for review because the current binding cannot re-check their shared passages.\n",
    "* **One evidence field.** Links cite only the objective; deliverables and reports were not used, so projects with short objectives are under-mapped.\n",
    "* **Reference quality.** The references are an AI reviewer, adjudicated by one person for batch A only; batch C is not adjudicated. More human reviewers would tighten the estimates (Q&A 15). Samples are small, so intervals are wide.\n",
    "* **Model proposals.** No link has been validated by a person unless marked; the published map is exploratory.\n",
    "* **Scale.** 400 of the 84,625 projects in the three CORDIS datasets. The method runs on one computer; mapping all of CORDIS needs more compute, not a different method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d5d21c1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T11:42:23.051380Z",
     "iopub.status.busy": "2026-10-23T11:42:23.051318Z",
     "iopub.status.idle": "2026-10-23T11:42:23.053630Z",
     "shell.execute_reply": "2026-10-23T11:42:23.053462Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples of open links (uncertain or insufficient evidence):\n",
      "- Cell2Carbox × 9.b: Cell2Carbox may contribute to 9.b (Support domestic technology development, research and innovation in developing countries, including by ensuring a conducive policy environment for, inter alia, industrial diversification and value addition to commodities), bu\n",
      "- AgriCapture × 9.b: AgriCapture may contribute to 9.b (Support domestic technology development, research and innovation in developing countries, including by ensuring a conducive policy environment for, inter alia, industrial diversification and value addition to commodities), bu\n",
      "- VIOBOND × 9.b: VIOBOND may contribute to 9.b (Support domestic technology development, research and innovation in developing countries, including by ensuring a conducive policy environment for, inter alia, industrial diversification and value addition to commodities), but a \n",
      "- nZEB Ready × 9.b: nZEB Ready may contribute to 9.b (Support domestic technology development, research and innovation in developing countries, including by ensuring a conducive policy environment for, inter alia, industrial diversification and value addition to commodities), but\n",
      "- OpeningUpScience × 16.10: OpeningUpScience has insufficient evidence to establish a contribution to 16.10 (Ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreements). No contribution hypothesis is establis\n",
      "- MSCA-NET × 9.b: MSCA-NET may contribute to 9.b (Support domestic technology development, research and innovation in developing countries, including by ensuring a conducive policy environment for, inter alia, industrial diversification and value addition to commodities), but a\n"
     ]
    }
   ],
   "source": [
    "unc = m[m[\"confidence_level\"] == \"uncertain\"][[\"project_id\", \"acronym\", \"assigned_concept\", \"assessment_state\", \"explanation\"]].head(6)\n",
    "print(\"Examples of open links (uncertain or insufficient evidence):\")\n",
    "for _, r in unc.iterrows():\n",
    "    print(f\"- {r['acronym']} × {r['assigned_concept']}: {r['explanation'][:260]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a346aa0e",
   "metadata": {},
   "source": [
    "## 8. Reproducibility and reuse\n",
    "\n",
    "* **Model.** `mlx-community/Qwen3.5-9B-4bit` at revision `8b2b98c00a6b4d291155e4890773ca8f769aee53`, run locally and offline, temperature 0, no paid API (Q&A 5, 12). Prompts, rubric sets and code are versioned; every decision records its contract, rubric hash and model revision.\n",
    "* **Verify without the model.** The repository carries a trace kit: for every run, the exact code that ran, its inputs, activity sheets, decisions and the one recorded model answer accepted for each job. With repository access, one command, standard Python, about 25 seconds:\n",
    "  `python3 scripts/mesh-verify.py`\n",
    "  It replays every recorded answer through its run's frozen code (5,085 decisions, 1,489 activity sheets), rebuilds the published map byte for byte, recomputes the validation figures and the method's chronology from git, and, given the official archives, rebuilds every source text from them. Model calls: 0. Every change to the repository runs it in CI, and the site cannot be built with a map it did not verify.\n",
    "* **Reuse by CORDIS.** Inputs are the public CORDIS archives; outputs are plain CSV and Excel (one row per link, per project, per organisation, per assessed pair) and the JSON the prototype reads. The rubrics are readable text that domain experts can edit and version; re-running the verifier shows exactly which links a rule change moves. Any reuse beyond evaluation is subject to agreement with the author.\n",
    "* **Access and terms.** All rights reserved. This submission, its code and its outputs are provided to the European Commission for evaluating the CORDIS–SDG challenge. The source repository is private for security. The author is open to offering the code for reuse by CORDIS or the European Data Portal on request, and evaluators can request access to run the verifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf870dbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-23T11:42:23.054637Z",
     "iopub.status.busy": "2026-10-23T11:42:23.054580Z",
     "iopub.status.idle": "2026-10-23T11:42:23.056944Z",
     "shell.execute_reply": "2026-10-23T11:42:23.056761Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "verified: True\n",
      "sheets_reproduced                              1489\n",
      "decisions_reproduced                           5085\n",
      "source_texts_rebuilt_from_official_archives    2581\n",
      "model_calls                                       0\n",
      "map rebuilt byte-identical: True\n"
     ]
    }
   ],
   "source": [
    "v = json.load(open(input_file(\"trace-verification.json\")))\n",
    "print(\"verified:\", v[\"verified\"]); print(pd.Series(v[\"totals\"]).to_string()); print(\"map rebuilt byte-identical:\", v[\"map\"][\"identical\"])"
   ]
  }
 ],
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