{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fef75586",
   "metadata": {},
   "source": [
    "# CORDIS → SDG Mapping — Methodology & Results\n",
    "\n",
    "**Competition:** *CORDIS – mapping EU-funded research projects to the Sustainable Development Goals* (EU Vocabularies / data.europa.eu).\n",
    "\n",
    "**Goal:** a transparent, explainable and reusable method that links CORDIS projects to SDG\n",
    "concepts from the official EU Vocabularies SDG taxonomy (`http://data.europa.eu/sdg`),\n",
    "preferring the **most precise relevant concept** (goal → target → indicator) and recording\n",
    "the evidence and confidence behind every assignment.\n",
    "\n",
    "This notebook explains the data preparation, the mapping logic, the results and the\n",
    "validation. The full pipeline is reproducible from the scripts in `work/`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca32f82f",
   "metadata": {},
   "source": [
    "## 1. Reference taxonomy (EU Vocabularies SDG)\n",
    "\n",
    "The taxonomy was retrieved live from `https://data.europa.eu/sdg` (RDF/XML). Because the public SPARQL endpoint is protected by a WAF, each of the 812 concept URIs was retrieved via linked-data fragments and merged into one graph. The taxonomy is a SKOS `ConceptScheme` with 17 top-level *Goals*, *Targets* and *Indicators* linked by `skos:broader`/`skos:narrower`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00160aff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:09.156814Z",
     "iopub.status.busy": "2026-08-29T20:29:09.156661Z",
     "iopub.status.idle": "2026-08-29T20:29:09.171268Z",
     "shell.execute_reply": "2026-08-29T20:29:09.170597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SDG concepts loaded : 812\n",
      "By type             : {'Concept / Series': 394, 'Concept / Indicator': 232, 'Concept / Target': 169, 'Concept / Goal': 17}\n",
      "Top-level goals     : 17\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from collections import Counter\n",
    "tax = json.load(open('sdg_tax/taxonomy.json', encoding='utf-8'))\n",
    "tc = Counter(' / '.join(v['types']) for v in tax.values())\n",
    "print('SDG concepts loaded :', len(tax))\n",
    "print('By type             :', dict(tc))\n",
    "goals = json.load(open('method/sdg_goals.json', encoding='utf-8'))\n",
    "print('Top-level goals     :', len(goals))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019a81b5",
   "metadata": {},
   "source": [
    "Every assignment later records **both** the top-level goal and, where supported by evidence, the most precise narrower concept (a target). Example — Goal 7 and its targets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "134a9434",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:09.172900Z",
     "iopub.status.busy": "2026-08-29T20:29:09.172710Z",
     "iopub.status.idle": "2026-08-29T20:29:09.176407Z",
     "shell.execute_reply": "2026-08-29T20:29:09.175803Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GOAL 7: Ensure access to affordable, reliable, sustainable and modern energy for all\n",
      "   target 7.2 - By 2030, increase substantially the share of renewable energy in the global en\n",
      "   target 07.0a - By 2030, enhance international cooperation to facilitate access to clean energ\n",
      "   target 7.1 - By 2030, ensure universal access to affordable, reliable and modern energy ser\n",
      "   target 7.3 - By 2030, double the global rate of improvement in energy efficiency\n",
      "   target 07.0b - By 2030, expand infrastructure and upgrade technology for supplying modern and\n"
     ]
    }
   ],
   "source": [
    "g7 = [u for u, v in tax.items() if v.get('notation') == '7'][0]\n",
    "print('GOAL 7:', tax[g7]['en'])\n",
    "for n in tax[g7]['narrower']:\n",
    "    print('   target', tax[n]['notation'], '-', tax[n]['en'][:78])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e666d772",
   "metadata": {},
   "source": [
    "## 2. The mapping method\n",
    "\n",
    "The method is a **controlled-vocabulary-grounded, multi-signal scoring** approach. It is fully transparent (no black box) and reuses authoritative vocabularies.\n",
    "\n",
    "**Signal types**\n",
    "1. `label` – an official SDG concept label / alt-label appears in the project text.\n",
    "2. `syn` – a curated domain term (from a thesaurus grounded in the SDG meanings) appears in the text.\n",
    "3. `vocab` – an **EuroSciVoc** title assigned to the project (a controlled vocabulary) matches an SDG concept. This is the strongest, vocabulary-driven signal.\n",
    "4. `topic` – a CORDIS topic title matches an SDG concept.\n",
    "\n",
    "**Fields used:** project title, acronym, objective (eGrant), keywords, EuroSciVoc categories and topics.\n",
    "\n",
    "**Assignment rule (avoids isolated-keyword matches):** a project is linked to an SDG concept only if it has\n",
    "  * a `vocab` / `label` / multi-word `syn` signal (strong), **or**\n",
    "  * at least **three** independent single-word term signals.\n",
    "\n",
    "**Confidence:** `strong` (vocabulary / label / multi-word evidence or many signals), `medium` (weaker multi-signal evidence). Assignments resting only on generic single-word terms are flagged `uncertain` for human review. The most precise concept (target) supported by evidence is reported instead of the bare goal whenever possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f1c3aa5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:09.177901Z",
     "iopub.status.busy": "2026-08-29T20:29:09.177699Z",
     "iopub.status.idle": "2026-08-29T20:29:09.181849Z",
     "shell.execute_reply": "2026-08-29T20:29:09.181223Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Curated synonym patterns : 839\n",
      "Official label patterns  : 38\n",
      "EuroSciVoc domains bridged: ['medical and health sciences', 'agricultural sciences', 'engineering and technology', 'natural sciences', 'social sciences', 'humanities']\n"
     ]
    }
   ],
   "source": [
    "m = json.load(open('method/sdg_method.json', encoding='utf-8'))\n",
    "print('Curated synonym patterns :', len(m['syn_patterns']))\n",
    "print('Official label patterns  :', len(m['label_patterns']))\n",
    "print('EuroSciVoc domains bridged:', list(m['domain_bridge'].keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecb7e7b8",
   "metadata": {},
   "source": [
    "## 3. Data preparation (CORDIS)\n",
    "\n",
    "Project data was downloaded from the official CORDIS distribution on data.europa.eu (`cordis-h2020projects-csv.zip` and `cordis-HORIZONprojects-csv.zip`). Fields used: `id, title, acronym, objective, keywords, EuroSciVoc, topics, startDate, endDate, fundingScheme, frameworkProgramme` and the coordinator `country` (from `organization.csv`). Text fields were lower-cased; the EuroSciVoc and topic titles were matched exactly as controlled terms. Missing/empty fields are handled gracefully (they simply contribute no signal)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ec4f2d5",
   "metadata": {},
   "source": [
    "## 4. Results — coverage and distribution\n",
    "\n",
    "The mapping was executed on the full combined H2020 + Horizon Europe corpus. A project id may appear in both monthly snapshots, so results are de-duplicated at project level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43298ef4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:09.183465Z",
     "iopub.status.busy": "2026-08-29T20:29:09.183260Z",
     "iopub.status.idle": "2026-08-29T20:29:09.961162Z",
     "shell.execute_reply": "2026-08-29T20:29:09.960644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unique projects : 58840\n",
      "With >=1 SDG (any)    : 34622\n",
      "Confidence split      : {'strong': 38951, 'medium': 19658}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confident coverage    : 29657 (50.4%)\n",
      "Strong-only coverage  : 27309 (46.4%)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "agg = json.load(open('output/aggregates.json', encoding='utf-8'))\n",
    "print('Total unique projects :', agg['total_projects'])\n",
    "print('With >=1 SDG (any)    :', agg['projects_with_sdg'])\n",
    "print('Confidence split      :', agg['confidence_counts'])\n",
    "# conservative coverage: projects with at least one NON-uncertain assignment\n",
    "df = pd.read_csv('output/mapping.csv')\n",
    "conf_proj = df[df['uncertain'].astype(str) == 'False']['project_id'].nunique()\n",
    "print('Confident coverage    :', conf_proj, '(%.1f%%)' % (100*conf_proj/agg['total_projects']))\n",
    "strong_proj = df[df['confidence'] == 'strong']['project_id'].nunique()\n",
    "print('Strong-only coverage  :', strong_proj, '(%.1f%%)' % (100*strong_proj/agg['total_projects']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5631369",
   "metadata": {},
   "source": [
    "### 4.1 Projects mapped to each SDG goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "959e3a15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:09.962726Z",
     "iopub.status.busy": "2026-08-29T20:29:09.962529Z",
     "iopub.status.idle": "2026-08-29T20:29:09.989212Z",
     "shell.execute_reply": "2026-08-29T20:29:09.988613Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "for f in ['fig_goals','fig_confidence','fig_concepts']:\n",
    "    display(Image(filename='output/figures/%s.png' % f))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f743b58d",
   "metadata": {},
   "source": [
    "### 4.2 Breakdowns by programme, year and country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0811fa1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:09.990719Z",
     "iopub.status.busy": "2026-08-29T20:29:09.990522Z",
     "iopub.status.idle": "2026-08-29T20:29:10.013173Z",
     "shell.execute_reply": "2026-08-29T20:29:10.012618Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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l7zVVKpjSUCrQi0qH9iWlhPxxEZn2NfDTwu6Fv35jwizPkj8q3O7s3l8SDjtZEoYcJ/YGqbW094jmAD75rKslZeTldbJPAAAAiN3sgyGEQC0xYcI+1pyyNUwvM6SvTDH0Ji3Fkd5MbPGJId9nRC8Nq1w9B9X4fhpKJR1zpjl5t22Swp8/l4KfPhPv+pXmds/SP80p978PSny/w0yYFX/QP8QWn1ALfwUiKRDVXn0Ve16VZzPrMbQXAAAAtY0AC6gl2hOmJpJHXW3qDznSmjEzHMJChwsmn3qJJI0YI8Vrl5leWYU/f2FmqNRhhkWzp5mTLTFZ4gcdLQmH/9OEZnuaWRLRRWcq9axcIAXTPqpmAG+Zoasa2O9LgAoAAABUFwEWUEt0GFdNuHr0l7jmbXg+EHZaE83Zvrs5pZx7vXiWzJaCHz+Tol+/MYXftQB84Q8fm5M9rakkDD3BhFlxHXpEZD01VE3roXlWzBP34tmmyL9n+XwRT1GtB/YAAABATRFgAbVEaxDZGzWuRg0sm5lhT9cHIo3Nbjc9a/RkXXyHFM35UQo1zNKZD4s94svcJrs+f82cHAd0NL0Itfg7YWxk0hlN3cvmimfx7ya00t5W+jzWdWAPAAAA1BQBFlCLNYl0Fri9F0G2zHrM4oVIZ3PFS8LgY8xJe9wU/vatv/j74tkiliXejasl/51nzMnZta8kHK7F348Xe8O0cO96zPLtyjM1zExYpafViyrMOll69knXgQPEdeBAievaVzJvG1WtSSh0xksCeAAAANQ2AiygFiWPGCOeFQvErb1VKtg9Db0WQAaiifa4STrqdHPybt8shb98YcKs4rXLze2e5XPNKfeVh8XV5xB/z6yBRzL5QC3TYNG95A8TVmloVfzXEhHLV3FFm03i2nYR54ED/aFVjwFib5ReZpXqBfAiroyhBPAAAACodQRYQC3SXlWNrn9cto0+uEKvBx02qF8Qmb0L0c7RpKU5jvVUvG6FmcVQZzP0bd9sjnsNcPWUm5DkL/5+2Eni6n2w2Bz8F7S/fDmZ4l4yW9yLtIfV71K8brnpDVeBzS5x7bubsMrZc6C4uvc3M1BWP4CvejbCwhlfSsKwUyQ+Y8h+/z0AAABAVWyWVdknXdS2RYsWSa9evWThwoXSs2dPGrwe86xaJDtvPdMs6+xuzs69TO8VHXLDsEHUV5bPZ4aumZkMZ34tVn5Omdu1Plz80BMk8fCTJa5TL4q/V5M3c5t4lvxhCq5rDyvv+pWVr2h3iLNTT3GWDAk0NfmSG9T8eSz2SP7HL0vBV2+b2QbLDBvsNViKZnwl4vOKuBIk7fbnmIkQAAAAtZZ9EGCFCQFW7Nj1+euSO/lhs9z42a8obo2YY3ncUjT3Zyn88VMp+mO6iMdd5nZHy3aScNjJ/uLvLduFbT8jkXfHlmD9KhNYbfqr8hUdceLs3NsfVvUcYGqQ2ROTQ7YfGmR5ls0xQxRLB/CFv34j2f++0YRYtvhESb3jBXH1OChkjwsAAIDotiiEARbjN4Ba5l76pzm3pzUVR7PWtDdijs3pMvWv9OTLz5WiWd+Znlnuhb/5i79vXiv57/2fOTm7ZPjDrKHHm15ascb798Zg7yoNrbxb11e+otMlzi59Soqua2DVp1bri2lYpTNRlpdw8LFiXf2Q5Dx9q1hFBZL10FhJu+sl8zwCAKJPVT9YAEAkIMACapGO0NXhPsrZvT/DpBDzdBhb4hGnmpN3x1ZTP8kUf9di4zrkdsV8c8qd/Ii4MoZIwuH/lPgBR4S0N1EkvT94t6wL9q7S4MrUDauMK0Fc3fruHhLYubeZFTISJB56kkixR3Im3SlWQb5k3n+ppN39shnCCACIDmbI+NSX/EPGs3eUGTKeeNxZpi4iQRaAcLNLFJk+fboJACo7/frrr2XWnTFjhhx66KGSlJQkLVq0kHHjxkleXl6FbRYVFcktt9wirVq1ksTERBk8eLB8++23lT5+dbcJBOiX08CHAC2aDGA3R+PmkvzPC6Xxo1Ok8b8/keTTLhV70wP8N/q84p77s+Q8fYtsu2SYZD91sxT9+aP5gB3NgVXxxtWy69v3JPvJm2T7ZUfKjnEnSs7zd0vhj5+UCa9sCUni6nuopJxzraTd/4Y0mzzThEIpp481Pa4iJbwKSPzHCGlw2b1m2dqVK5n3jRHPmqXh3i0AQDXo/61Zj44zM8/6sneWuc2XtcNcnzXxmqj+PxhA/RCVPbA0OBo4cGCZ6zp37hxcnjt3rhx11FHSo0cPeeKJJ2TDhg3y2GOPyYoVK+TLL78sc78LL7xQpkyZItdee6106dJFJk+eLCeeeKJMmzbNhFX7sk0gQItYBzipCwNUKa51J0k5+xpJPmucGboQLP6emyVSVGBmNdSTrWG6GV6owwx1mJr+gBHJheyL168Uj84SWDIksPSv2qXZkhqIs0d/07tKA6q4Dj2ibpbGpKNOFykultyX7jNF+zMnjJH0e1+RuLZdwr1rAIA90J5X7jk/llwqP7+X/7LOSKuTeqSMvJy2xD5jiCr2V3R9Oi5x2GGHyemnn17l7bfffrukpaWZHlsNGzY017Vv314uueQS+eabb+TYY481182aNUveeecdmThxotx4443mugsuuMAUGLv55ptNj6uabhMozb10jjm3JSbzJQ6oBg2ktLeinhpceKu45/0iBT99LkW//0/EUyRWzk4p+Ootc3I0b2MKv2uYFXdAh7C3r+X1SvG65WYooGfRbHEv/cMfwFXCltJIXD0GmILrGlrFte0qNodDol3ScWeZ4YQ6cYWVmymZEy6WtPGTJe6AjuHeNQBAFYGCDhsU0R+E9jQ5vc2slzz8YoYSosYYooqYDrBUbm6uGfIXF1f2T8jJyTFDAK+77rpg0BQIpvS69957Lxg2ac8rh8Mhl156aXC9hIQEufjii01gtX79emnTpk2NtglU1gPLFFiOst4UQCQUf9f6V3ryFeT7i7//+Jm4F/yq3ZtMgfP8D543p7hOPUuKv58gjrSmdfILoOUtNrW7AvWrPEv+NMPnKv1bGqbvLriugVWbzmKzR9Uo/mpLOul80755bzxuepxljv+XpI1/lRkmASACuRf9XmXv4LIs8WVtN/93VjapB7C3Iar+Xn62SoeoelYskNSbniIcxV5F5Tfqiy66yNSe0vBJe2NpD6oBAwaY2xYsWCDFxcXBywEul0v69u0rc+b4e8QoXe7atWuZUEoNGjQoOGxQA6yabBMI0BoCgSnvtYA7gH2nRdwThw03J2/mtt3F31ctMrfreZ6eXp0oroyDTZgVP+joCsXf9+cXQBN6rVoknsAsgcvmmKLlle5vWtNgwXUNrRwHdIzo4Y6hljz8X/62fudp8WVuMyFW+vhXxdGcmVgBIJy1GH1/bxTPSv+EKRoaeFYtrNE29IcfoCYYooqYDbA0MBo5cqSpUdWkSRNZvHixqUOlIZYO9+vXr59s3uwvgtuyZcsK99frfvrpp+BlXbeq9dSmTZuC61V3m5X5+++/Zdu2bWWuW7lyZTX/akQr97LdwSYF3IHQ0R5WySddYE7FG//y18v6+XPTI0t7ZrnnzTAncU0wvbcSDz9ZXH0OMfetyS+AlsctnpULdg8JXD7X1OOqjL1xC//sgCVDAh0t2sZUYFWZlJGXmeGE+VOeE9+OLbJz/EX+EKtpq3DvGgDEBF9+rgmoAjP8Fq9cUM3eVlXTXstAdTFEFTEdYA0dOtScAk455RRTCysjI0Nuu+02+eqrr6SgwP/lIj6+4gxNOjwwcLvS5arWC9xe+rw626zMpEmTZPz48TX4S1GvCrg74syU9wBCT2tfpZx1tSSPusp8ODdh1i9fmvpL4i6UohlfmpOtQao4mrWW4uAvzVUXqc28/1L9mVo8K+aJeNyVPq7W3/L3sCoJrJqVzJ6IMpLPvNJ8eN019SXxbdsUHE6oM1ACAELHDGvXiUNKwio9eTeuNv+fVf4fWZyZMCSuUy8p+ulTsXbtfWZ1W8mQe6C6TLkGhqgiVgOsyujsg8OHD5cPP/xQvF6vqYulioqKKqxbWFgYvF3pclXrBW4vfV6dbVbmiiuukDPOOKNCD6wRI0ZU869ENAdYZjaxhKRw7w5Q/4u/d+1jTg1G3yzu+TP9YZYWfy8qMMXUi6soqF6eZ9GsCtc5Wrb316/qWTIksHGLWvgr6ufzknLOtSYI3PX5a6aXnD/EmlytWmUAgMp5d2z1DwVcPs8/FHD1oip7CQd/eOnc28zgG9clQ5ztu4vN5f9xPi+1semFvDdWXrbkvfWkmTVY61QCoR5yyhBV1PsAS2mdKrfbLfn5+cFhfoFhf6Xpda1a7R66oOtu3Lix0vVUYN2abLMyzZo1MyfEDquoQDyrF5tlhg8CdUuH/8X3P9ycGmjx99nTZNfnr5fqfbV39iatJL7/Yf5hgRpYEbbsX4g1+mb/MIKv3xbv5jVmdsL0e18Re6PG+75hAIgRVuEu87lSewYHalf5dm6tcn1bUoOSsMofWDk7Z4i9UXqV62v9R92m9kKuOBthyWWb3QzT3/XpZDNMv+G4h8XZrluI/1LUN8XrV9VofYaoIiYCrNWrV5uhfCkpKdKrVy8zM+Hs2bPlzDPPDK6jAZcWZS99nRZgnzZtmpllsHQh999++y14u6rJNgGlHy7EW2yWKeAOhLn4+2Enm1+Zsx+7ttr3a3DhzZIw+Jha3bdYC7Ea/Ot2875Y8N374t2wSjInjJE0DbEapIZ79wAgYlg+nxn6pzUY/b2r5puhgeLzVn4Hu0Pi2nU1IVUgsHK06lCjmW71hx+t/5j/8cv+SU6ytu/efGpjSTz+bIkfeJTkTLrDTJpSvG657Lx1lKScNU6STh4tNocjFH866hFfTqbk/vdBKfzli2rew2aONYaool4FWFoIvWnTskMO5s2bJ5988omccMIJYrfbpVGjRnL00UfLG2+8IXfddZc0aNDArPf666+bmQtLD+XT+llaBP7FF1+UG2+8MThM8JVXXpHBgwebnl2qJtsElDtQ/8r0wKJWABBuNf1Fj18AQ0+/TDW45G7TE6tw+lTzBSjzvjGSdvfLtDeAmKX1gUrXrfKsXChWQdX1qHTSENOrKnDqeKDY4vdczqS6IVbKyMslefjF/rpFednmvVkDhcDsvOn3vyn5H7wg+R++aCbpyHvjcSn680dpdNWDTNCBoMKZX0vuyw/srn3ljBfxVCzFU5ZlgtKqZoIGojLAGjVqlKk3pYXcdUiezkKo4VNSUpI8/PDDwfUeeOABs86wYcPk0ksvlQ0bNsjjjz8uxx57rBx//PHB9TSk0vBJC8DrTIFaT+vVV1+VNWvWyMsvv1zmsau7TaB0/Sutm8MQGSD89AO4vhZ92TsrKeBeGr8A1naI1fDyCaYnltYoK/5riSman3bXS2JP9v84BAD1leUuEs9fS/y1qzSsWj5ffNsqljMJ0GBKi6yXDqwc6bVblkQDBFfPQVXeljLqKnH1O0xynrlVvFvWiWfx77LjhlOlwcV3SMLh/4z5GXhjmQZWOS8/IEUzvw5ep7NBp/zrDsl96b4qhqj6ufoPM+EpsDc2y6pqaorI8/TTT8ubb75pCqDrsD/tjXXUUUfJPffcY8Kn0n7++We55ZZb5M8//zQ9pnSY30MPPRTsPVW6CLv2qtLeVZmZmWZGw/vuu0+OO+64Co9f3W1Wx6JFi8zQxIULF0rPnj33oTUQybPAbLtwiKlXkHDkSGk0dkK4dwmAFqmd8ly1itQmn3W1+RUatfs+mf3UzcEPuc5ufSX1jhfNkE8AqA/0K5YJeEr1ripeszRYYqICm00crTv5hwJ29YdVca07ic0Rmf0NfAX5kvfaRDMsPCD+4GOl4aX3MDQ8Bo91/f8856X7/bNAl8xYqaUDEg49yYSa2vu6siGqtoRkSRrxLxNe0fuq/loUwuwjqgKs+oQAq/7SX9Z23ny6WW54xf2SeMSp4d4lAPoBq9gjWROv2WORWv0FUOuA8CGqbp6P7CdukKLfvzeXnT0GSNrtzzFrK4Co5MvN8tet0pMZCrjAzIBbFe0VXLpnlfa0sielSLQp+mO65Dx3d3C4mD2tqfn8G9/30HDvGuqAN2u76V1V9Nt3weviBx8tDcbcJY7UJpX+36+lVrL/faNYOTvFmXGwpN9VduQT6p9FIQywIjPSB+rB8EFFAXcgclSnSC2/ANbt89Housck67FrTajoWTJbsh65SlJvnSS2+IQ63BMAqBnL4zZ1/AIzAuq5zrBaJWe8ODv2KOld1ccEVvYmLevFcLv4g/4hjR+fKjkv3CNFv/9PfJnbJOuByyTxuLOlwfk3hKQ+FyKP9oHRAu1a68rKyzbX2RqkScMxd0r8kOOqPLbNTNG9BkvCwCOl4Psp4lkyxwyt1cl2gOogwAJCzL3kz+Ava44WbWlfIIJUp0gt6vD5cLok9YZ/S9ajV4t73i/iXvibZE28WlJvfpYPswBCQnt87M/7vX5R923fHJwR0NSvWr1kj0WptQaqv2eVzgrYx8wSWJ//j7E3SpdGNz0thdM+ktxXHjJlNAq+flvcC36VRuMeFmenXuHeRYSQN3Ob5P5nggksA+KHHC8NL77DHAvV4eoz1ARY+jrSHlnxGUN4jlAtBFhACOmHnEAPLO19VR9+WQPqoz0VqUXd0l9dU296WrIevsIEWO55MyTr8esk9cYnTcAFAPvC1NyZ+pK/x21gNjTT47aJJB53liSPGFNpqOTblSfFqxaWqV1V+v4V3sM0FAsMBezc25zsDVJj7knTz7yJR54mzp4DJeeZ20xo6N30l+y841xJPmOsv70jtJ4XatDr6sdP/SFlfo65ztYw3fS6ShhSsX70nrh6DTZ138SyxD1/BgEWqo13ESCEfH9vFF/m32bZ2aM/bQsA1aBDBlNveVYyH7xcPEv+MEMKs5+8yQwxrM+9FgDUYs3DR8eJe86PJTUOd/Nl7TATeujQv0bXPyHeLWuDMwJq7yrvhlXmS3WlHHES17572VkBW7TlB8tS4pq3kbTxr8quT/4reTpxirdY8t95Rtx//CgNr35I4lq247CPQt4dWyXnP+PF/YfWEfVLOOREU6jd3jCtxtvTkFfrvhWvXGB+uJLzbgjxHqO+IsACQki7wAa4uhNgAUB12RKSJPW25yTr/kvFs3yuFM36TrKfvlUaXfMIv9oDqBHteeUPr1T5MMp/WYPybaMHVz0roH7JbnpAcEZAU7+qQw+GN1fn/dzhkORTLxFXn0Mk++lbxLtxtXhWzJOdN42UlAtvkcSjTif0i6ZeV9OnSu7kR8TalRssk9Lg0nskYdBR+7VtHTaoAZbOzqm9HHW7wN4QYAEhFBg+qAUr9Rc6AED12ROTJfX25yXzvjFmCE/RzK8kJy5OGl75oPlCBADV6X2lwwYrzjZbiVLhlS0xRZyde+2eFbBz70pnUUP1OTseKI0feV/y3vq37PriDbGKCiT3hXulaPY0aXj5BNo3wnm3b5acF8eLe85PwesSDvunNLjo1pAMk9WAM//DF81y0fyZknjYyfu9TdR/BFhACLmX/GHOdYYZxvkDQM3ZkxtI2p0vSuaEi6X4ryVS+NNnZthOw7H3ic1up0kB7JEp2L6HmlXlJQ3/lyQOGy6OAzryHlNLQ8QbXHSbuPoPk5z/u8OU2tBhaDtuOFUaXj7ezEaHCOx19b8PJffVR8UqyDPX2dOaSsNL75H4AUeE7HE0KNYf/TXYdBNgoZr4JAiEiC8n03SRDhRwBwDsG50pLO3O/0hc267mshm+8J8J5kM1AOyJe9m8GjWQ6W3VpjPhVS2L7zNUGj/+kcQPPcFctnJ2SvajV0v2c3eLryC/th8e1eTdtkmyHrhUcp6/OxheJfxjhDR+4uOQhldKJ2px9vJPqKN1sPg/HtVBgAWEiHvZnOCys8dBtCsA7ActCpt290umV4Qq+O59yf3vg3zABVApLcSe+cBlkv/2kzV7r0lpRIvWER12lnrdY9Jw3CNiS2pgriv83wemNlbpz9Goexoe7fr2Pdlx/XB/UXV9vtKbm2H9ja58oNZeJ/EZQ8259swzEygAe0GABYS4/pXYHWYKZQDA/tGCrmn3/FccLdubywVfvSV5OqSBnlgAAp+/Vi2SzIeukJ23ny3uuT/XoF1sYk9tIs5u/WjLOqa1jrQ3lqvXYHPZu3W9ZN51geS99ZRYHjfPRx3z/r1Rsu4bI7kvjhercJe5LuHIkf5eV/0Oq9XHdvXxB1iqaL4/OAP2hAALCBHPUv8vR3EduptCxACA/edIa+oPsZq3MZd3ff6a5L31JCEWEOM8fy2RrEeukp23nmlmFDQccZJ43FmS9M8Lq7EFSxKPP1tscc7a3lVUwtGkpaTe9ZKkjL5ZxOkSsXyS/9GLsvOOc6WYnjh1wvL5ZNfXb/t7XS341Vxnb9xCUu94QRqNnWBqUtY2R6sO5jGVe97MWn88RD+KuAMhYBUVimfVQrPsov4VAISUo3FzE2LtvGe0+LZtkl1TXzK1M1LOvJKWBmKMZ+1yyX///6Tot+92X6nB1ZGnSfKpl4ijaSszE2Hxxr9Kgq3ysxH6L2tR8eThF4fjT0DgmbDbJfnk0WYYWfbTt0jx2mVS/Ndi2XHzGdLg/Bsk8bizqU1WS4q3rDN1rjyLfg9el3j0GZJy/o1iT0qps2PUZrOJK2OoFE77UNyLfzc98PT/d6Aq9MACQsCEVyVTMVPAHQBCT7+UaogV+KU2//1JkvfBCzQ1ECOK16+UrCdukJ03nro7vLI7zFCnJk9/YWZI0/cJpb2qUm96SpLPulrsqY3LbEcv6/V6O72vIkNc2y6S/tA7kqSBos0m4ikyNQ+zHrhMvDu2hnv36l+vqy/flB03nhYMr+xNW5necA0vu7dOw6uA+D5D/AtFBeJZPrfOHx/RhR5YQCjrX2mARS0FAKgVcc3bmBArU3tiZW6T/HeeNl9Ak4f/ixYH6qnijasl//3npHDGl1pp2n+lzS4Jw06R5JGXSVyLtpXeT98bUkZebnpZeZbNEV9etilErZ/TCK4ij/a6aXDe9RJ/0DDJfuZW09vWPX+G7LhhhAknE4YeH+5djHrFm9dKznN3iWfJH8HrdMhtyrnXh7X8iav3EH9waVlSNG+GuHr6ZyYEKkOABYSAuyTAcrRoa+q1AABqR1zLdiUh1oXiy94heW88br6MJp10Pk0O1LMv2ya4+vlzUx8pGFwdepIkn365xLXyT+6wN/r+wBfi6OHqcZA0fuwjyX3lISmcPlWs/BzJ/vcNUjR7ujS4+HaxJzcM9y5GHcvrlV1fviF5bz8t4i401zmatZaGYycEC+mHe9bhuA4HSvHqRf4ZEM+5Nty7hAhGgAWE4D8FzzJ/d1eGDwJA7Ys7oKOk3f2y7Lz3QrFysyR38sMiGmIddxbND0S54q3rJX/K81L446ciPq//SptNEoaeIMlnjDWvf9RvOoyt0ZUPSPyAf0jOC/ea9/nCnz41NZIaXfVgRIQu0UJrweU8d2fwu4pKPOFcSTn7moiadMqVMcQEWFoDzZeTaUItoDLUwAL2U/H6FWLtyjXLFHAHgLqrmZJ210tiK/k1Pvel+6Tg+w9ofiBKef/eKNnP3S07xp1ket4Ewqv4IcdL48enSqNrJxJexZiEwceY597V7zBz2bdji2ROuFhyX5solrso3LsX8T+w53/yiuy4aWQwvNLZfNPGvyoN/3V7RIVXKr7PUP+CZQVnRAQqQw8sIJT1r5iBEADqjLNDDxNi6Rca/SEh54V7ROLiJHHYcJ4FIEp4t22S/I/+IwX/+zA4IY6KH3y0JJ9xhTjbdQvr/iG8tDRH6m3PScE375rgSofA7fp0srjn/iINxz0szvbdeYrKKd6wSnIm3SmeFfP9V9hsknTieabXlS0+MSLby9QQ1n0rKpCi+TMl4ZATwr1LiFAEWECI6l/ZGqaLo5r1GAAAoeHs1FPS7nxRMu8bI1ZBvuT8350ijjhJPPQkmhiIYN4dW/zBlfacLPYEr48fcIQkn3mlCagBZdMA5rizxNX7YMl++hYpXrXQjIDYedtZknLWOEk6ebTYHI6YbyzLW2zCvbz3/k/E4zbt4WjZThpecX/EjxLRIv6uAweIe85P4p73i1iWZZ53oDyGEAL7Qd9cPUv8AZb+x8AbLQDUPWeXDPMLvfll2fJJzjO3SeGv3/BUABHIm7lNcv77oGy/+gQp+PqdYHjl6j9M0h9+V1JveZbwCpXSwv3p979heuaJ3WGOHZ3II3P8v8wQ1FhWvG6F7LzjXMl789/+8Mpml6R/XiSNJ34Y8eFV6TpYgaGi3k1rwr07iFAEWMB+8G3fLL6dW82ys3s/2hIAwjhzVeptk0RcCaZ2TvaTN0nh7//j+QAihDdru+ROfkS2X3mcFHz5ZrCHiKvvoZL+4NuSdtskcXbqFe7dRITTWSVTzrzSBFnau0h5lsyWHTeeKgU/fGx+XI4lloZ4H7wgO245w/RMUw6d6OT+N6TBBTeKLT5BokV8n0OCy+75M8K6L4hcBFhACIYPqmj5dQMA6itXz0GSevMzIk6XqaWT/fh1UvTnj+HeLSCm+bJ3Su7rj5ngatfnr4l4/MW3Xb2HmC/ZaXe8YHpRAjWhx0zjR6dI4jGjzGUzhPzZ2837vi83KyYa07N2mey8/RzJf+dpf09G7XU1/GLTLq6ufSTaOFp3EntaM7NcNI8AC5UjwAL2Q2D4oP7iH0etBgCIiJmMUm96WiTOaUKsrMeu4YMwEAYaIuS++W/ZfuWxsuuTV0zxbeXsOUjSJrwmaXe/JC4t3AzsI1tCkjS89G5JvXWS2Bs1NtcV/fat7LhhhBTN+al+97p6f5LsvOVMKf5rcTD8SX/gTWlw3vVic8VLNNJSLIFhhJ5Fs8zfCZRHgAWEYAZC/RVIuzQDAMIvvt9h0uj6J0wxdx2mlPXIVeJe+Fu4dwuImeAq7+2nZPsVx8iuqS+JVVRgrnf2OEjS7vmvpN/7ihnyC4RK/EHDpPHjUyV+4FH+YzBzm2Q9eLnkvHRf8PirLzx/LZGdt46SfC3UrrN22h2SfOqlptdVfejJ6Ooz1Jxbhbt2z6IIlEKABewjX162mQFFMXwQACJLwsAjpdG1E/2Ffj1FkvnwleJe8ke4dwuot3z5OWb2Mx0qmP/hi+YLqHJ26yupd70kaeNfFVevweHeTdRT9kbp0uimp8yMe9ozS+kkATtuOl08KxdItLM8bsl791kz82Lx2mXmurg2XUz9uJRzrjGz+NUHOtNkgM5GCJRHgAXsI8+yucFlZw/qXwFApEk4+FhpePVDpi6IFBVI1kNjxb18Xrh3C6hXfLvyJG/Kc/7g6v1JYhXk7Z4d9I4XJO2+NyQ+YwgzNaNOhqAlHnGqNH7sI3GW1Kb1bl4jO+88zxyjlvZYikKeVYv8va6mPOfvdeWIk+TTx0r6I++Js1NPqU8cqU0krl03s+yePzPcu4MIFBfuHQCiVfCXfJtdnF2ir1AiAMSCxENPMh/4c/7vDlPkN+uBy0ztHWY7A/aPryBfCr56S/I/eUWsvOzg9XEde0rKqKvE1e8wQiuEhaN5a0m7d7Ls+uS/pteS/h+Q/+6z4v7zR2l49cMSVzJ7YTT0usp//znJ//hlM7uu0nCn4ZUPiLMe197VYYTay8yzcqEZ8WJPaRTuXUIEoQcWsJ/1r+LadxN7UgrtCAARKnHYcGl42XizbO3Klcz7LjF1RADUnA4N1C/U2uMq760ng+FVXPvu0ujmZyT94Xclvv/hhFcIK5tDa0NdIukPvWMKnCutqbTjppGy69v3xLKsiH6GdNjjjlvOkPyPXvSHV9rr6swrzd9Tn8Mr5crw18ESy0f9SlRADyxgH1juIvGsWmiWA12UAQCRK/GokWZGo1wt6pufI5n3jZH0eydLXNsu4d41ICpoMexd37wruz7+r/iydwSvj2vb1Xyxjh94pNjs/DaOyKJhT+NH3pe8t/4tuz5/3Qwnz31xvBT9MV0aXj7BDFmLtO8YWkvOzNxp+cx1cR0OlIZX3i/OkqF19Z2pLeyMN/Ur3fNmmHIAQAD/ywD7wIRXJVO7MpMOAESHpOPOkgYX3mqWrdwsyZxwsRRvXB3u3QIimlVUaL74mx5Xr00Mhlfaq0Vn+0yf+IEkDD6a8AoRy+aKN+/9qXe/LPb05uY69x8/yI7rR0jh7/+TSKE1GnfcfLrs0iGDGl5pr6uzxkn6g2/FTHilbPEJwe9XRfNmRHxvOdQtAixgP4YPKme3frQhAESJpJPOl5TzbzTL+kU8c/y/pHjz2nDvFhCR9Xd2ffmmbL/6BMmd/PDu4KpVBzPDpxbKThhyHMEVokZ874Ol8eMfScIhJ5rLVm6mZD96tWQ/d7ep6RbOkDj3tcck887zxFvyo0pcp57S+NEpkjLyMrHFOSXWaB0s5du2Ubxb1oV7dxBBGEII7AP30jnm3NG8jTjSm9GGABBFkk+5yPSizXv7KfFlbjMhVtr4yRLXvE24dw2IiOCqYNpHkv/hi+LbsSV4vaNlO0k+4wpJGHqCqS8ERCMtCK4BrGvAPyT3pfvNkPLC/30gnoW/mVlrzfC1OuRe+qfkTLrLzJZoxDkl5cwrJemUi8TmiN2v6q6MIcFlnY0wWgrvo/bRAwuoIcvnE88yf4Dl7E7vKwCIRsmnXWqmIVf6JV1DLO+2TeHeLSBstEbcru/el+3jTpTc/0wIhlf6Y53Oetb4359I4mEnE16h3sxQ2/ixD8XV+2Bz2fv3Bsm8e7TkvfWUCXHroqac9mzMvPuCYHjl7JJhel1p8flYDq8CtfXsjRqbZa2DBQTE9isD2AfeDavMrzWKAu4AEL208LT50j71JfFt21TSE+tVcTT210gBYoHlLZbCHz+V/CnPmy/xAfamB0jK6ZdLwuH/jMkhTKj/HE1aSuqd/5GCL9+U3DefEPG4zax/RXN/kkZXPyxxbTrXyuO6F8+WnOfu2j00zumSlLPGSdJJFxAQl9AJIbQXVuFPn5mZCPV9KtZDPfjRAwvYh66+AXXdzRgAEDo2m01SzrlWkk4ebS57t67398TK3EYzo97TL4QFP3wsO645WXIm3RkMr+yNW0iDS++RJk99JolHnkZ4hXoflGhtRJ2pMK59d3Nd8V9LZMctZ8quL94wIy9CxSrcJTn/fVAy7xkdDK+c3fpK44kfmqHtDM2tvA6WVZAnnpULQvY8ILoRYwL7WMDd1iBVHAd0pP0AINpDrAtuMj2xCr56ywzl0BArffzk4PAFoD6xvF4pnPGl5L//3O66OxpcpTc3Q2tNaOV0hXUfgbqmva3SH3xb8t77P/8sgJ4iyX3lISn64wdpeMX9+90z171oluQ8d7f5ocRwJUjK2eMk6YTzCK6q4Opdtg6Wi4mzQA8sYN97YOmbqH7xAQBEN30vb/Cv2yXxmDPNZZ0FKnPCGPHlZIZ714CQ0Z4khb98KTtuGCE5T98SDK/saU3N8d/kmS8l6bizCK8QszS4bXDudWYouQ6hVe75M8xrRl87+0JnN8x56T7JvPeiYHilJUi0/lbyyaMJr/ZAJ8qKa9PF/zzM+2Wf2h/1Dz2wgBrwbt9s6qQo6l8BQD0LscbcZXpiFU77SIrXLZfM+8ZI2j3/NbNWAdEcXBX99q3pcVW8fkXweu1hmHTqGEk6+kyxxSeEdR+BSOLqcZAJmLTIuv5/oLVvs5+8UYpmT5MGY+4Ue3LD4Lr6f4ZO7uTLyzb/Vzj1B+6SmnFFC341va582zb6V45PlAbnXCuJx59jhi6iGs9Fn6HmfcuzYoH48nPFntyAZotxBFjAPta/IsACgPpFv1A0vGy8iIZYP30mxWuWSub9l0raXS/xoRlRx7IsKfr9f5L/3rNSvHZ58HpbgzRJHnGxJB07SmwJSWHdRyBS2ZNSpNEV90v8Qf+QnBfuFSs3Uwp//lzcS/6QRlc+IM4eB0n+1Jek4Ku3xZe9Y/f9UptIwpGniS97pxR+PyV4vfPAgdJw7ASJa9E2TH9RdNJC7rs+e1XE5zXDMBMGHRXuXUKYEWAB+1D/Spzx4ux4IG0HAPWMFtFteOUDYhUXS9HMr6R41ULJevBySb3zRbEnJod794A99vgIBFfuP6abWj5ajDrAltJIkof/SxKPO5tjGaimhMFHi7NrH9OTyj3nR/Ht2CKZEy4We7MDxPe39qwqW07El7Vddn344u7XXXyipJx/gyQeM4peV/vAdeAAEX1/K/aY4ZwEWCDAAvYhwHJ2yaBGBADUUzpVd6NxD0u2t1iKZn0nnuVzJeuhsZJ2+/P0WEFYg6uqenwkHneWJA2/WDwLf5O8d5+R4lWLgrfbkhuaGc4STziX4ArYB460ppJ62yQp+O59yX31UZGigpLwyrwyq75fs9aSdvfL4mjemnbfRxoA6qzv7oW/iXveDNoRwuBboJp8+TlSvM5fO8LVvR/tBgD1mPZoaXTtRHEdNMxc9iz5Q7IeuUqsosJw7xpiNLzKenSc5L/7rBmaVJova4e5fvuYw01vwUB4ZUtqIMlnXilN/u8bM7sgPQiB/auTmHTMmZL+0Dsijur1AbHchWLfz9kL4a+Dpbxb1ol36waaJMYRYAHV5Fk+T/vlm2XqXwFA/aczUqVe/29x9TnEXNZfgLMmXi2Wuyjcu4YYoz2vdPhS5T0+/JetXbnm3JaYLMmnj5Umk76RlDOuoH4bEEJWzk4Rb3G11tXhhDrcF/vHleEPsFTRfHphxToCLKCaPEtK6l/ZbOLs2pd2A4AYYHPFS+pNT4ur12BzWYcwZD1+nVged7h3DTHU+0qHDZavtVPVcJvGT30hKaOuKjNTGoDQ0Npztbk+Kopr391MPqEYRggCLKCGMxDGte3Kr5kAEENs8QmSesuz4uwxwFx2//mDZP/7RhMsALXJ8nrNjJj+mlfW3tcvKhDvptU8KUAt0YkTanN9VD5DsCvj4GBPaH1fROwiwAKqQX9p96xcYJYZPggAsceWkGSK+AZ64Bb9/r1kP32LWNUcSgLsjR5LWmuzYPpUyfnvg7LzznPl79GDJWfSnTVqPHp8ALVHZ/20N2pcjR6RNjPBgq6P/RdfMpTf0prEq3dPUoHYwyyEQDV4Vi8W8fhrnrh69KfNACAGaRHs1Nufl8z7xkjxqoVSNPNryYlzSsMrHxSbwxHu3UO0hVUbVpsvYvoZo3j1YvGsWSri3v9JAujxAdTuBB+Jx59tJk7YM8usp+tj/7kyhpSpg6UzwiM2EWAB1eBZ+kdwmR5YABC77MkNJO3OFyVzwsVS/NcSM7xLZ6RqOPY+M8wBqDysWuUPqVYt8p+vXbbnsMoZL8723SSuQw+Ja9dd8t5+Uqy8nL0MI9QeH43p8QHUsuQRY8SzYoEZTu7viVX6dem/7Oo/TJKHX8xzESKOxi3EcUBH8W5c7a+DNfJy2jZGEWAB1eAuKeBub9rKvIECAGKX9nBJu+slybz3Iilet1wKp08VmyNOGlx6jwmxtDaWzjylQ7l0XR1Cwq/wsUGf++L1K/0h1epFJuT0rFkW7MVddVjVXeI6HijOTj3NedwBHcscM76cHfT4ACKEvjZTb3pK8j9+2UywoLMNBmiIrD2vNLzifT/0sxEWbFxtZob3FeSbXtGIPQRYwF5YPl9wClxXd4YPAgBE7A1SJe3ul2TnvReJd8MqKfh+ilh2hzjSmkrB12+XFN320zooicedZX615wtN/aqPqT2rTFC1qiSwWrdcZE8zVLoSdodVGlR17ClxrTuaAHRP6PEBRBZ9L08ZebkJqvjBom7E9xkqBV++IeItFs+iWRI/4Ig6emREEgIsYC+0q6pVMgWus8dBtBcAwNBCvml3vyyZ91wo3s1rpPDbd0tapmxxX1+Wv/eMDjnRX+0JsaI0rFq/0h9SBXpXrV0usqeZKOMT/cMAO/Y0YZWzY09xHNBhr2FVZejxAUQmfW26eg4K927EBOeBA8yQfQ2wiubPJMCKUQRYwF64l/qHDyp6YAEAStMeV2n3/Fd23DDCzI7kV75Okf+y1kvRISf6qz0iPKxat9xfXH3VIvH8tcTfs2oPYZUtPtHUqwr0qtJzR6sOIS3uT48PALFMhwzqkHzP4t/9dbAQkwiwgL3wLPUPH7QlNzTFAwEAKM3eKF3EXp2gwmbqpVAbJXJY7qLdYVVgRsB1K8wv/FWxJSRVDKtatq+zmSjp8QEglmcj1ADLu+kv8W7bJI6mrcK9S6hjUT1dzgMPPCA2m0169epV4bYZM2bIoYceKklJSdKiRQsZN26c5OXlVVivqKhIbrnlFmnVqpUkJibK4MGD5dtvv6308aq7TdQvnpIeWM7u/ZhhCgBQ8f+JZXPEys2sRstYpthv/if/FfeyOVK8aY34crNMrUXUTVjlWTFfdn39jmQ/d7fsuGmk/H3BINl521mS+58JUvD9B6boeunwyqa/+PcYIEknXSANxz0ijZ/8VJq++pukT3hNGlx4qyQe/k+Ja92pzsIrAIj1OlgB7vkzw7ovCI+o7YG1YcMGefDBByU5ueLsA3PnzpWjjjpKevToIU888YRZ97HHHpMVK1bIl19+WWbdCy+8UKZMmSLXXnutdOnSRSZPniwnnniiTJs2zYRV+7JN1B/eHVvF+/cGs8zwQQBAZXS2wZrIf/tpyS99hc0utpSGYm+QJvYGjcSWkir2hmliN+epYmuQWnJbavBkS2m0T7WUIlFtzNpoFRWKZ+2y3b2q9LRh1Z57ViWmSFzHHuLsoD2rSmpWtWzHj1cAECHiOhxoRsXokH2tg5V41Mhw7xLqWNR+8rnxxhvl4IMPFq/XK9u37566VN1+++2SlpYm06dPl4YNG5rr2rdvL5dccol88803cuyxx5rrZs2aJe+8845MnDjRbE9dcMEFpkfXzTffbHpc1XSbqF8Csw8qJzMQAgAqoaHLfrF8YuVmiVdPNbibfogPBlol4Zc/9EorCb0Cp5LLGno5XREVXOVPfck/Df1+zNpoFRWIZ00lYZWv6ta0JTUoGQbYU5yd/EMBHc3bEFYBQATT3q46jLBo5tfiXjDT9GC22aN6UBliIcD68ccfTa+pOXPmyNVXX13mtpycHDME8LrrrgsGTYFgSq977733gmGTbsPhcMill14aXC8hIUEuvvhiE1itX79e2rRpU6Ntop4WcHe6xNmp4lBVAAC0x5DOSOjL3llJAffSbGJrmCqpNz8j1q48M3zQl5tpwitfTpb48rL8y7mZ4svNNuficVe5Nf0F2qunLeuq/STokDh/oNWoTK+u0r28KoRfrvhaCa+yHh0n7jk/1mjWRn9YtdQ/E+Aq/4yAxRtX7zmsSm4ozg49gr2q9JywCgCiUyDA0v8vddi3s1PPcO8S6lDUBVja40pDqzFjxkjv3r0r3L5gwQIpLi6WAQMGlLne5XJJ3759TegVoMtdu3YtE0qpQYMGBYcNaoBVk22inta/6tQron61BgBEDg1YEo8/24Que2ZJ0onniatbv2pt17IskaKCkqArEGwFQi4NvTL9oVdJ+OW/nG3uU+U2C/LFW5AvUjI8vlriE8v15qo6/Aqc9D5ap7Qq2vPKH17526XcXgZnbcz5z33ibNdVPCW9q7wbVpsea3sMq0xx9XJh1R72BQAQPeIzhkpuybJ7/gwCrBgTdQHW888/L2vXrpXvvvuu0ts3b95szlu2bFnhNr3up59+KrNuVeupTZs21Xiblfn7779l27ZtZa5buXLlHu+D8PPtypPiNcvMMsMHAQB7osPdtMeQhi7+HkWlQxn/ZVf/YWYGwuoyoUtCkjj0VIOZlrT+U+lAa3evLn9PLyt42+7LGmxVSUM0PW33fx6qFqcrWNMrOIQxEHglN5Rdn0yu1mYK//eBFFZxm9YBC4RUGlrpsr3ZAYRVAFCPOZodYOoTejevlaJ5MyT51EvCvUuoQ1EVYO3YsUPuvvtuueuuu6Rp06aVrlNQ4P/VMT6+Ynd3HR4YuD2wblXrld5WTbZZmUmTJsn48eP38tch0niWzw3+yksBdwDA3nph6XC3/I9f9td0ytpdn9Oe2tj00NLwan+Lk1eHLT5BHPEtxNG4RbXvY3ncJuzSYGt3wFVyOdgDrNSQRw3G8nOq3qBub+dWcwrJ39QgtaRnVc/dYVXTVoRVABCDXBlDpWDzWjNaxircJbaEpHDvEupIVAVYd955p6Snp1eoe1VaYmKiOS8qKqpwW2FhYfD2wLpVrVd6WzXZZmWuuOIKOeOMMyr0wBoxYsQe74fIGD4oNps4u/Xl6QAA7JGGUykjLzdBVahn1attOkzekdbUnKrL8haLVRJyBcIu/5DG3TW8ygx51PBLQy8dGlkDDS65WxKPOZOwCgBguPoMlYKv3zYzy7qX/CHx/Q6jZWJE1ARYK1askBdffFGefPLJ4NC+QIDk8XhkzZo1ppZVYJhfYNhfaXpdq1a7u+Druhs3bqx0PRVYtybbrEyzZs3MCdFZwD2uTZf9n2EKABAzNKxy9fTX06zPbI44sTVqbArYV5fl9UrRH9Mle+K4at8n7oAOhFcAgCDzf6zdYSbwcM/7hQArhkTNnJMaNPl8Phk3bpx06NAhePrtt99k+fLlZnnChAnSq1cviYuLk9mzZ5e5v9vtNkXZteh6gC7rfXWWwdJ0m4HbVU22ifpBh1JoLRPl7F69YrsAAGDvU6DH9z+8JPTaW2F1m9hTm5gebAAABNiTUsTZtY9ZLpo/k4aJIVETYGmI9NFHH1U49ezZU9q2bWuWL774YmnUqJEcffTR8sYbb0hubmB+ApHXX39d8vLyygzlO/30082shtqzK0CHCb7yyisyePBgMwOhqsk2UT8Ur1kq4vYPJaWAOwAAoZ+1seLsg+VZZr1IH34JAKh7rowh5ty7fqV4d4Sm3iIiX50NIdTeSjrULzk5eZ/u36RJk0prRumQQlX6tgceeECGDh0qw4YNk0svvVQ2bNggjz/+uBx77LFy/PHHB9fTkErDp9tuu83MFNi5c2d59dVXzXDEl19+uczjVHebqF/DBxUF3AEAiPxZGwEAscPV5xDJf+//zLJ7wUxJ/Af1pWNByHtgvfPOO3LdddeVuU5n4EtJSZHU1FQ59dRTTa+l2tS/f3/57rvvTHF13RftYaW9s6ZMmVJh3ddee02uvfZa05tKhydqyPbZZ5/J4Ycfvs/bRPTzLPEHWPbGLWo0dTkAAKj+rI3JZ11tZmksTS/r9Xo7va8AAJVxduoptqQGZtk9bwaNFCNsllXDqWD2YuDAgdKvX7/gsLwZM2bIoYceKieddJL06NFDnnnmGRMYPfTQQxLLFi1aZIZFLly40AyDROTQl8S2iw8TKzdTEg45URpdOzHcuwQAQL1lFXuibtZGAED4ZT12jRT99p2pq9jkxelis0dNhaSYsiiE2UfIhxCuWrVKRo8eHbz81ltvSYsWLUyNKi2EroXYP/jgg5gPsBC5vJvWmPBKOXv0D/fuAABQr8XKrI0AgNByZQw1AZYve4cUr10mzg49aOJ6LuQRpRZBT0hICF7+5ptv5IQTTjDhlTrwwANN/SggUrmX/hFcpoA7AAAAAEQeV5+hwWX3fIYRxoKQB1gdOnQwtaLU7NmzZeXKlWWKnG/dutXUwwIivf6VjqmOa9053LsDAAAAACgnrnkbcTRvY5bd82bSPjEg5AHWZZddJu+9955kZGSYGfpat24tJ598cvD2X375hZpPiGiekhkInd36is3hCPfuAAAAAACqGEYYGEVjFRXSRvVcyAOsq6++Wl544QXp1KmTDB8+3Awh1Jn71M6dO2XLli1y7rnnhvphgZDwZm4T79b1ZtnV4yBaFQAAAAAilKvPEP+Cx12mFAzqp5AXcVeXXHKJOZWXnp5uhhUCkd77SlH/CgAAAAAil6vXYBGbXcTyiXveDInvc0i4dwnR1ANLe18tXbo01JsF6jbAinOKs1MvWh0AAAAAIpQ9uaE4O/c2yxRyr/9C3gNr7NixYrPZTG+rQw89VA477DBzOuigg8RuD3leBoSUe+kcc+7s1FNsrnhaFwAAAAAifDZCz4p5Urx2uSkJ40hrGu5dQi0JeaKkNa7effddU+dq3bp1cvPNN8vBBx8sqamppqj7fffdJ9OnTw/1wwL7zVeQL8V/LTHLDB8EAAAAgMjnyhiyu0PCgl/Dui+Ish5YzZo1k9NPP92cVG5ursyYMUN++uknmTJlitx7772mh1ZxcXGoHxrYL54V883YaeXq3p/WBAAAAIAI5+ySIbbEZLEK8k0drMTD/xnuXUI0FXEPWLVqlQmu9PTjjz+ay8nJyTJkyO6EFIjIAu7d+oV1XwAAAAAAe2eLc4qr5yApmj3N1MGyLMt0mkH9E/IA69lnnw2GVjqcMFALS2tjaS2s/v37i8PhCPXDAvvNs8Q/7aqjdSexN0ilRQEAAAAgSupgaYDly9ouxetWiLNd13DvEqIhwBo3bpwJqEaOHCk33XSTKd4ORDqr2CNuHULI8EEAAAAAiCqujKHBZe2FRYBVP4W8iPuVV14pvXr1MvWuDjnkENP76rbbbpMvvvhCsrOzQ/1wQEgUr1kqUlRglingDgAAAADRw9GyndibtjLLWgcL9VPIA6xnnnlG5syZIzt37pQPP/xQDj/8cPn555/ltNNOk8aNG0vfvn3l6quvDvXDAvvFXar+lasHvQYBAAAAIFpozav4ktkI3Uv+EMtdFO5dQjQEWAENGzaUE088UR588EF57bXX5Omnn5YuXbrI/PnzZdKkSbX1sMB+FXC3pzcPJvcAAAAAgOjg6nOIf8FdKO5lc8K9O4iWWQgXL14cnHlQzzdu3Giub9WqlZx11lmmmDsQKXSWCs/SOcHhg8xYAQAAAADRxdVrsHbF0i94ZhhhfO+Dw71LiPQAq0mTJpKZmWlCge7du8sJJ5xg6mBpaNW+fftQPxyw37xb1okve4dZdnXvR4sCAAAAQJTRmeTjOvaU4lULTSF3kevDvUuI9ABr9OjRJqzS0ErDLCBahg8qCrgDAAAAQHSK7zPUBFjFfy0RX/ZOsTdKD/cuIZJrYD3++OMyYsQIwitEDfcSf4BlS0yWuLZdw707AAAAAIB94MoYGlwuWjCTNqxnaqUGlvrhhx/k888/l7Vr15rL7dq1k5NOOkmGDRtWWw8J7FcPLGfXvmJzOGhFAAAAAIhCzq59xBafKFZRgbjnz5TEQ08K9y4hkgMst9stZ599tkydOtXUwUpNTTXXZ2Vlmd5Zp556qrz99tvidDpD/dBAjWntK+/mNWbZ2aM/LQgAAAAAUcrmdImz5yBx//mDKeSumQSTdNUfIR9COH78ePnoo4/khhtukM2bN8vOnTvNacuWLXLjjTfKhx9+KBMmTAj1wwL7xF2q/pWrOwEWAAAAAER7HSzl27lVvBtXh3t3EMkB1ltvvWUKuT/66KPSvHnz4PXNmjWTRx55RC644AJ5/fXXQ/2wwD7xlNS/EkecODv3phUBAAAAIIq5MoYEl7UXFuqPkAdY2utq8ODBVd6ut2lvLCCSemA5Ox5oxkoDAAAAAKKX44COYm/cwiwXzaeQe30S8gCrdevWMn369D0Wd9d1gHCzCneZ6VWVs/tB4d4dAAAAAMB+0ppXgV5YnkWzxPK4adN6IuQBlg4ffO+99+Tyyy+XZcuWidfrFZ/PZ5bHjh0r77//vlx44YWhfligxjwr5ov4vGaZAu4AAAAAUL/qYOlshJ7l88K9O4jUWQhvv/12WbVqlbz44ovyn//8R+x2f0amIZbOAKABl64DRFQB9659w7ovAAAAAIDQcPU6uEwdLFfPgTRtPRDyAMvhcMjkyZPl+uuvly+++ELWrl1rrm/Xrp2ceOKJkpGREeqHBPaJZ+mc3WOkG6XTigAAAABQD+j3u7gOB0rxX4ulaP4vknLONeHeJURagLVr1y457LDD5JJLLjFDCAmrEKksb7F4ls81y67u/cK9OwAAAACAENI6WBpgFa9eLL7cLLE3SKV9o1xIa2AlJSXJX3/9ZYqmAZGseO1yU8RdObv3D/fuAAAAAABqoQ6WWJa4F/xK29YDIS/ifvzxx8vXX38d6s0CIeUpVf+KAAsAAAAA6hfzPc+VYJbd82eEe3cQiQHWXXfdJcuXL5fzzz9ffv75Z9m4caPs3LmzwgkIJ/eSP8y5PbWJOJq34ckAAAAAgHrE5nSJ68ABZrlo3gwzqRyiW8iLuPfs2dOcL168WN56660q1/N6vaF+aKBa9I0r0ANLU3mGvAIAAABA/ayD5Z77s/i2bxbv5rUS16p9uHcJkRRg3X333QQCiGjerevFl7XdLLuofwUAAAAA9VJ8n0MkTyaaZfe8XwiwolzIA6x777031JsEaq/+VY+DaF0AAAAAqIccbTqLPa2p+DK3iXv+TEk64dxw7xIiqQYWEC0Bli0hSeLadQ337gAAAAAAaoGWi9FhhMq9aJZYxR7aOYoRYCHmuJfOMefOrn3F5gh5J0QAAAAAQIRw9TnEnFsF+eJZMT/cu4P9QICFmOLL3inejavNsrN7v3DvDgAAAACgFrl6Hxxcds+bQVtHMQIsxBT38rnBZQq4AwAAAED95khtInHtupllrYOF6EWAhZjiWVJSwN3uEGeXjHDvDgAAAACglrkyhppzz8oF4svLpr2jVMgDrNdee03WrFlT5e16m64DhLOAe1yHHqaIOwAAAACgfnP18QdYYvlMMXdEp5AHWBdddJHMmFH1uNLffvvNrAPUNauoQDyrF5llhg8CAAAAQGww3/+cLrNMHazoFfIAy7KsPd6en58vcXHM/Ia6p91FxVtslp09+vMUAAAAAEAMsMUniKvHALNMHazoFZIkaf78+TJ37u7i2D/99JMUF/uDgtKysrLk+eefl65du4biYYEacZcMH1TObsxACAAAAACxwpUxRNzzZ4h363op3rJO4lq0DfcuIRwB1kcffSTjx483yzabTV544QVzqkxqaio1sBDWAu6Olu3MTBQAAAAAgBiqg/XG48FeWARYMRpgXXrppXLyySeb4YODBg2SCRMmyAknnFBmHQ22kpOTpVOnTgwhRJ2zvF7xLPf3EnR2P4hnAAAAAABiSFzbrmJv1Fh82TtMHaykY0eFe5cQjgCrZcuW5qSmTZsmPXr0kGbNmoVi00BIFK9bLlZBvll2Uf8KAAAAAGKKzW43wwgLf/pM3At/E8tbLDYH9bljuoj7sGHDCK8QcTyl61/pDBQAAAAAgJiiAZayduWKZ+XCcO8OaqhW4savv/5aXn75ZVm9erVkZmZWmJlQhxOuWrWqNh4aqJR76Rxzrl1GHRTrAwAAAICY48oYGlzWgu6ubn3Duj8Ic4A1ceJEufXWW6V58+amHlbv3r1D/RBAjWiA6lnyh1l2du9nAlQAAAAAQGxxpDcTR5vO4l2/0tTBkjOuCPcuIZwB1lNPPSVHHnmkfPHFF+J0OkO9eaDGfNs2iS/zb7PM8EEAAAAAiF3xGUNl1/qV4lkxX3y78sSelBLuXUK4amDpkMHTTz+d8AoRw12q/pWL+lcAAAAAELNcfUqGEfq84l40K9y7g3AGWDpscNmyZVIbFi1aJGeccYZ07NhRkpKSpEmTJnL44YfLp59+WmHdJUuWyPHHHy8pKSmSnp4u559/vmzbtq3Cej6fTx599FHp0KGDJCQkSEZGhrz99tuVPn51t4nIEhg+KPGJEte+e7h3BwAAAAAQJq4eB4nE+UeLmWGEiN0hhJMmTZITTjhBBgwYIOecc05It7127VrJzc2V0aNHS6tWrWTXrl3ywQcfyCmnnCIvvPCCXHrppWa9DRs2mGCrUaNG8uCDD0peXp489thjsmDBApk1a5a4XK7gNu+44w55+OGH5ZJLLpGBAwfKxx9/bPZb6ySdddZZwfVqsk1EZg8sV5cMsZW8UQEAAAAAYo8tIUmc3fqJZ9EsU8gd0cNmlZ8icD9pD6adO3fK5s2bTU+l1q1bi8PhKPugNpvMmzcvJI/n9XrloIMOksLCQlm6dKm57oorrpDJkyeby23btjXXfffdd3LMMceUCbo2btxoel7p5WeffdZcp80xbNgw+euvv2TNmjXBfa/uNmvSm6xXr16ycOFC6dmzZ0jaAhX5crNk278OMcvJp4+VlFFX0UwAAAAAEMPyP/qP5L31pFlu8n/fiKPZAeHepXprUQizj5APIdShdV26dDG9lfr37y/NmjWTxo0blznpOqGiAVObNm0kKysreJ32yjr55JODQZM6+uijpWvXrvLee+8Fr9PeVh6Px4RTpcO1sWPHmh5XM2fOrPE2EVk8y+YEl53aVRQAAAAAENNcGSV1sESkiF5YsTuEcPr06VLb8vPzpaCgQLKzs+WTTz6RL7/8UkaNGhXsVfX333+bIYyV1efS2RED5syZI8nJydKjR48K6wVuP/TQQ2u0TURoAXe7Q5xdMsK9OwAAAACAMIvr0ENsDVLFys0ydbCSjj4j3LuEcARYdeGGG24ww/aU3W6X0047LTgEUIcuqpYtW1a4n16nwxuLiookPj7erNu8eXPT66r8emrTpk013mZlNPwqX+x95cqV+/S3o2Y8JQGWFm+3JybTfAAAAAAQ42x2u7h6D5GiGV+Ke8GvYnm9YitX+giRJ+RDCFVOTo4pjH7cccdJv379TJFzpUHPE088sd/hzbXXXivffvutvPrqq6ZgvNbBcrvd5jbtmaUqC5N0lsHS6+h5dder7jarKmyvYz5Ln0aMGLEPfzlqwioqFM/KhWbZ1b0fjQcAAAAAMOL7+IcRWvk5Urx6Ea0Siz2wtHaUFkFfv369qYWlRc91xj6lta+055TOJvjUU0/t82N0797dnNQFF1wgxx57rPzzn/+U3377TRITE8312iOqPC30rgLr6Hl116vuNiujNbbOOKNsl0QN8QixapdH34S8xWbZ2b1/LT8aAAAAACBauDKGBJeL5s+k5EwsBlg33XST5Obmyty5c00Bdz2VpqHNZ599FtLHPP300+Wyyy6T5cuXB4f5BYb9labXaYgW6Eml606bNs3MPFh6GGHgvq1atQquV91tVqaydkDdDR9UBFgAAAAAgABHk5biOKCjeDeuNnWwZORlNE6sDSH85ptvZNy4cXLggQdWqC2lOnbsaHpnhVJg+J4WdT/ggAOkadOmMnv27Arr6VDGvn37Bi/r8q5du2TJkiVl1tOeXIHbVU22icjhXuIPsBzN24gjrWm4dwcAAAAAEIGzEXqWzxVfQX64dwd1HWBpmKRhT1W0d9a+0mLo5Xk8HnnttdfMED4NzdTIkSNNL6/SQdn3339vemiVHso3fPhwcTqdpkZVgPbGev75501oNXTo7qk1q7tNRAYtwudZNscs0/sKAAAAAFBefJ+SYYTeYvEs/p0GirUhhBoi/fjjj2ZIX2WmTp1qCrvvC92mFog//PDDTcC0ZcsWefPNN02drccff1xSUlLMerfffru8//77csQRR8g111xjanBNnDhRevfuLRdddFFwe61btzYF4fU2DcIGDhxo9u+nn34y23WUmoWguttEZCjesFKsXf6w1NmD+lcAAAAAgLKcBw4UccSZAKto3kyJP+gfNFEsBVgaCI0ePVoyMjKCPZN8Pp8pWj5+/HiZOXOmfPDBB/u07VGjRsnLL78szz33nOzYsUMaNGggBx10kDzyyCNyyimnBNdr06aN/PDDD3L99dfLrbfeKi6XS0466SQTcpWvVaWzJaalpZni8pMnTzaF59944w0555xzyqxXk20isupfuSjgDgAAAAAox56YLM6ufcWzZLa458+gfSKczdIxcyH2wAMPyL333muG42l4ZbfbzbKe33///XLLLbdIrFu0aJH06tVLFi5cKD179gz37tQ72U/eJIW/fCG2BmnS9OWfKq3HBgAAAACIbXkfvCD57zxtlps8950p7o7IzD5C3gNL3XHHHXL++eebnlba80pDrE6dOslpp51mirgDtc1d0gNLe18RXgEAAAAAKhOfMSQYYLnnz5TEI0+joSJUrQRYqm3btnLdddfV1uaBKnm3bRLfji1mmfpXAAAAAICqxHXsKbbkhmLl50jR/BkEWLEYYCnteZWdnW2GD5aXnp5emw+NGBbofaWofwUAAAAAqIrN4RBX74Ol6NdvxD3/V7F8PrHZ7TRYBAr5s6Kz+WmdKx0qqMXNmzRpIk2bNq1wAmq9gLsrQeLad6ehAQAAAABVcvUZas6t3EwpXrOUloqVHliXXXaZvPrqq3LwwQfLiBEjpFGjRqF+CGCPPEvnmHNnl95ic7poLQAAAABAleIzhkpuybJ73i/i7HggrRULAdb7779vCrhPnjw51JsG9sqXly3F61eYZYYPAgAAAAD2xtHsAHG0bCfezWtNIffkUy+h0WJhCGFSUpLpfQWEg2f5PJGSmmvO7v15EgAAAAAAe+XKGBKsqWwVFdBisRBgnX322fLZZ5+FerNAtbiX/OFfsNnF2bUvrQYAAAAA2CtXn0P8C8UecS+eTYvFwhDCRx99VP71r3/JySefbM7btGkjDoejwnr9+9M7BrVXwD2uXVexJ6XQxAAAAACAvXIdOFDE7hDxecU9b4bE9zuMVqvvAVZRUZH4fD758ssvzak8y7LEZrOJ1+sN9UMjxlnuIvGsXGCWGT4IAAAAAKgue3IDcXbJEM+yOaYOFmIgwNJeVx999JGcddZZMnjwYGYhRJ3xrF5kunsqV4+DaHkAAAAAQLW5+gw1AZZODObd+bc40pvRevU5wPr666/l6quvln//+9+h3jRQreGDytmtH60FAAAAAKg2V8ZQyX/v/8yye8FMSRw2nNarz0XcGzZsKJ07dw71ZoG90tkilKNZa3E0bk6LAQAAAACqzdm5l9iSGvi/X86bQcvV9wDrkksukbfffpsaV6hTls8nnqVzzDL1rwAAAAAANWVzxImr12CzrHWw9Hsm6vEQwgMPPFA+/vhjM8vg6NGjq5yF8LTTTgv1QyOGeTeuFis/xyw7uzN8EAAAAABQc66MIVI06zvxZe+Q4nXLxdm+O81YXwOsUaNGBZdvvPHGStdhFkLU1vBB5erenwYGAAAAAOxTIffg98x5Mwiw6nOANW3atFBvEtgrzxJ/gGVLaSSOAzrSYgAAAACAGotr0VYczduId+t6M4wwefi/aMX6GmANGzYs1JsE9sq99I/g7IM2e8hLuwEAAAAAYmgYYcG368W9ZLZYRYVii08I9y6hNoq4A3XNu32z+LZtMsuuHgwfBAAAAACEYBihx12mXA3qWQ8s9fXXX8vLL78sq1evlszMTLEsq0INrFWrVtXGQyMGeZb5Zx9UzEAIAAAAANgfZiZCm12nuzd1sOJL1cVCPQqwJk6cKLfeeqs0b95cBg0aJL179w71QwBluEvqX4kzXpwde9I6AAAAAIB9Zk9uKM7OvcWzYp6458+gJetrgPXUU0/JkUceKV988YU4nc5Qbx6owFPSpVPfYGxOFy0EAAAAANjvOlgaYBWvXSberO3iSG1Ci9a3Glg6ZPD0008nvEKd8OXnSvG65WbZ1Z36VwAAAACAENbB0lE/C36lSetjgKXDBpctWxbqzQKV8iyfK1JSY81JAXcAAAAAQAg4u2SILTHZLLvn/UKb1scAa9KkSfLhhx/KW2+9FepNA1UOHxSbTZxd+9BCAAAAAID9ZotziqvnILPsnj+zwuR0qAc1sEaNGiXFxcVy/vnny9ixY6V169bicDgqzEI4b968UD80YpB7qX8Gwri2XUyhPQAAAAAAQlUHq2j2NPFlbhPv+pXmeyfqUYCVnp4ujRs3li5deGJRuyyPWzwr5ptlJ/WvAAAAAAAh5OpzSHC5aP4MAqz6FmBNnz491JsEKuVZvVjEU2SWCbAAAAAAAKHkaNlO7E1aim/7ZnHPmyHJJ4+mgetTDSygzutfMQMhAAAAACDEtPxRfMlshO7Fs80oINSjHlg//vjjXg+AhIQEUxurZcuWoX54xGCApYm4ownHEgAAAAAgtFwZQ6Xg+w9E3IXmO6ir98E0cX0JsP7xj3+YkKo6tE7W+PHjTeF3oCYsn0/cy/wF3F3UvwIAAAAA1AITWGnGYVlSNH8mAVZ9GkL41VdfSUZGhnTr1k0ee+wxmTp1qjlNnDjRXNe3b1+ZMmWKPP744yboOuecc8xloCa8m/4SKzfLLDt7HETjAQAAAABCzt4gVeI6HmiWtQ4W6lEPLA2wdIjgb7/9Ji6Xq8xtV1xxhemh9euvv8ojjzwil19+uQwYMMAsn3766aHeFdRjbupfAQAAAADqQHzGIVK8apEUr1kivuydYm+UTrvXhx5Yb775pulVVT68UhpsnXvuufLqq68GL5933nmyePHiUO8GYqT+lS25oThadwr37gAAAAAA6ilXnyH+BcsS98Jfw707MSvkAVZ+fr5s3bq1yts3b94seXl5wcupqanicDhCvRuo5zxL/fWvnN36ic3OZJoAAAAAgNrh7NpXbPGJZrlo3kyaOUxC/s3/yCOPlCeffFI+++yzCrd9+umn8tRTT5l1AubOnSvt27cP9W6gHvPu/Fu8W9ebZVf3fuHeHQAAAABAPWZzusR54ECz7J4/QyzLCvcuxaSQ18B69tln5YgjjpDhw4fLAQccIJ06+Yd3rVq1SjZu3Cjt2rWTZ555xlxXWFgo69atkzFjxoR6N1CPeUpmH1ROZiAEAAAAANSy+D5DxT3nR/Ht2GImFYs7oCNtHu0BVtu2bWXBggXy/PPPy9dffy1r16411/fo0UOuvfZaueyyyyQ5OTlYA+uLL74I9S6gnnMv8de/kjinODv1CvfuAAAAAADqOVdGSR2sktkICbDqQYClkpKS5PrrrzcnINQ8S/8w5xpe2VzxNDAAAAAAoFbp5GH29Obi27lViubPkKQTz6PF6xjVrxFVfLvypHjNMrPM8EEAAAAAQF2w2Wzi6jPULHsWzhLL46bho60Hlta7stvtZrhgXFxcmQLte3riv//++/19aMQgz4p5IpbPLLt69A/37gAAAAAAYmgYYeG0j8QqKhDPivniOnBAuHcppux3Dyytvu/z+QMFpct63Z5OpdcHasITqH+lPbC6MQMhAAAAAKBuxPcuXQfrF5o92npgTZ8+fY+XgVByL/UHWHFtuog9pRGNCwAAAACoE/ZG6RLXoYcU/7VEiubNkJSzr6Hl6xA1sBA1rGKP6aapqH8FAAAAAKhrrgx/Hazi1YvEl5vFExDNAda6devk559/LnPdvHnz5IILLpBRo0bJ1KlTQ/2QiBGacou70Cw7qX8FAAAAAKhjgULuYlniXvgb7R9NQwjLGzdunOTl5cl3331nLm/dutUUene73dKgQQOZMmWKvP/++3LaaaeF+qERI8MHlas7BdwBAAAAAHXLpbWYXQmmc4V73gxJGHIcT0G09sCaNWuWHHPMMcHLr732mhQUFJheWBs3bpSjjjpKHnvssVA/LGKAZ+kcc25v3ELsTVqGe3cAAAAAADHG5ooXV4+DzHLR/BlmojpEaYC1c+dOadasWfDyZ599JsOGDZNOnTqJ3W43Pa+WLl0a6odFPadvCoEeWM7u/cRms4V7lwAAAAAAMTyM0Ldtk3i3rAv37sSMkAdYTZs2lbVr15rlrKws+fXXX+W443Z3qSsuLjYnoCa8m9aIlbPTLDN8EAAAAAAQ7kLuyj3vF56IaK2BdfTRR8vTTz8tDRs2lOnTp4vP55MRI0YEb1+8eLG0adMm1A+Les5Tqv4VMxACAAAAAMIlrm0Xsac2EV/WdnHPnylJx5/DkxGNPbAefvhh6dGjh9x4443yzTffmHpXHTp0MLcVFRXJe++9Z+pgATURGD5oS0yRuDZdaDwAAAAAQFhoSZtALyydidAq9vBMRGMPrObNm8svv/wi2dnZkpiYKC6XK3ib9sb6/vvv6YGFfe6B5ezWV2wOBy0IAAAAAAhrHazCHz8RqyBfPCsXUOomGntgBTRq1KhMeKU00OrTp4+kp6fX1sOiHvJmbgsWxgvM9gAAAAAAQLi4eh8cXHbPm8ETEY0BlvawmjhxYpnr/vvf/0rbtm1N76zrrrtOvF7vPm37999/l6uuukp69uwpycnJZptnnnmmLF++vMK6S5YskeOPP15SUlJMYHb++efLtm3bKqynvcIeffRRM8wxISFBMjIy5O2336708au7TYSWZ9mc4DL1rwAAAAAA4eZIaypx7bqaZa2DhSgcQnjvvfdKu3btgpcXLFggl112mQmGOnfubAq8t2jRQm655ZYab/uRRx4xwxPPOOMMs70tW7bIs88+K/379zezHfbq1cust2HDBjn88MNNL7AHH3xQ8vLyTC0u3ZdZs2aV6Rl2xx13mLpdl1xyiQwcOFA+/vhjOeecc8yY1rPOOiu4Xk22iVoq4O6IE2cn/3MMAAAAAEA4aR2s4rXLxbNivvjyc8Se3JAnJJoCLO2lNHLkyODl119/3cxI+NNPP0lSUpJcfvnl8tprr+1TgHX99dfLW2+9VSYsGjVqlPTu3duEUG+88Ya5TgOm/Px8+eOPP0wvLTVo0CA55phjZPLkyXLppZea6zZu3CiPP/64XHnllSYIU2PGjJFhw4bJTTfdZIIyR0m9pepuE7VXwF3DK1t8Ak0MAAAAAIiIOli7Pp0sYvnEvXCWJAw+Oty7VK+FfAihhjwaWAV89dVXZtidhldKezmtXbt2n7Y9dOjQCj2dunTpYoYUanAW8MEHH8jJJ58cDJrU0UcfLV27djWzIAZobyuPxyNXXHFF8DrteTV27FjT42rmzJk13iZCy1eQL8V/LTXLzu79aF4AAAAAQERwdT9IxOnPKKiDFYUBVps2bUytKrVy5UpZuHChHHvsscHbd+7cKfHx8SF7PMuyZOvWrdKkSZNgr6q///5bBgwYUGFd7TE1Z87uekq6rLW0evToUWG9wO013SZCS2dzEJ+/Zhr1rwAAAAAAkUJHCJkQy9TBopB71A0hPPfcc2XChAkm9Fm0aJGkpaXJ8OHDg7frEDzttRQqb775pnksfUy1efNmc96yZcsK6+p1GqAVFRWZEE3X1cLy2uuq/Hpq06ZNNd5mZTT8Kl/sXcM91KD+labb3eiBBQAAAACIHK4+Q8S9YKZ4t66X4q3rJa55m3DvUr0V8gBLi6K73W754osvzHA7rQ+VmppqbtOgZ/r06XLNNdeE5LGWLl1q6lcNGTJERo8eba4rKCgw55WFSTrLYGAdvT1wvqf1arrNykyaNEnGjx+/z39nLPMs+cOcOw7oKPaGaeHeHQAAAAAAyhRyF3kiOBth3DEEWFETYMXFxckDDzxgTuWlp6ebmQNDQbdz0kknmVkBp0yZEiy2npiYaM61R1R5hYWFZdbR8+quV91tVkZrbGlB+PI9sEaMGFHtvzcWWcUe8SyfZ5Zd3fuHe3cAAAAAACgjrl03sTVMFytnp7jn/SJJx5xJC0VLgFUXsrOz5YQTTpCsrCwzu2GrVq2CtwWG+QWG/ZWm12mIFugppetOmzbN1NEqPYwwcN/Admuyzco0a9bMnFAzxWuXiVXk7/3m7EGABQAAAACILDa7XeIzhkjhz5+bmQgtb7HYHFEZtUS8WmlV7ZWks/b9+eefJmzy+Xxlbtew6OWXX97nbf/zn/+U5cuXy3fffScHHnhgmdsPOOAAadq0qcyePbvCfWfNmiV9+/YNXtbll156ycxgWHo7v/32W/D2mm4ToeMuXf+qpDAeAAAAAACRxNVnqAmwrPwc8axaJK6ufcK9S/VSyAOstWvXyhFHHCFr1qwxta80wNIeStpbyuv1mtkCU1JS9mnbev9Ro0bJzJkz5eOPPza1ryozcuRIefXVV2X9+vVmVkT1/fffm9DruuuuC66nxeX1staoevbZZ8112hvr+eefN6HV0KFDa7xNhI5niT/Asqc1E3uzA2haAAAAAEDEcfXenU3obIQEWFESYN10000mtPr111+lY8eOZujcu+++K4cccog8/fTTJij6+uuv92nbN9xwg3zyySemB5YWhH/jjTfK3H7eeeeZ89tvv13ef/99E6Rpwfi8vDyZOHGi9O7dWy666KLg+q1bt5Zrr73W3ObxeGTgwIEydepUMyxRZzcM1NWqyTYRGhokBmYgdHbvX2GmSAAAAAAAIoGjcXNxtOks3vUrxT1vhsjpY8O9S/VSyAOs//3vf6Zo+aBBg0zIFAgjtEaUhls6XE9Do88//7zG2547d645//TTT82pvECApT2kfvjhB7n++uvl1ltvFZfLZQq+P/744xVqVT388MOSlpYmL7zwgpkxsUuXLiYYO+ecc8qsV5NtYv95t6wTX/YOs+yi/hUAAAAAIIJpHaxd61eKZ8V88e3KE3vSvo08Qx0GWLt27ZL27dub5YYNG5qeM9ojK0CH/d144437tO3p06dXe92ePXtWq6eX3W6X2267zZxCtU3sv0Dvq0APLAAAAAAAIpWrzyGy6/PXRbzF4l40SxIGHhnuXap37KHeYNu2bWXDhg1mOS4uztSS0uGEAYsXL5aEhIRQPyzqGffSOebclpgscW27hHt3AAAAAACokqvHQSJxTrPsnj+TloqGHlhHHnmkKbB+zz33mMsXXnihPPTQQ5KZmWlmI3z99dflggsuCPXDop4J1r/q2ocpSAEAAAAAEc2WkCTObv3Es2iWvw4WIj/A0vpQv//+uxQVFZnaUFr8fNOmTTJlyhRTFF1rSz3xxBOhfljUI1r7yrvpL7PM8EEAAAAAQDSI7zPUBFjezWvEu22TOJq2Cvcu1Su1MoRw5MiRwcLmOlzwpZdeMj2wtm/fbgqla20sYG/DB5WL+lcAAAAAgCjgyhgaXC6aTy+siA+wgJAVcHfEibNLBg0KAAAAAIh4cR16iK1BqllmGGHoEWAh4rgD9a86Hii2+MRw7w4AAAAAAHtls9vF1XuIWXYv+FUsr5dWCyECLEQUq3CXFP+1xCxT/woAAAAAEE3iM/wBlpWXLcV/LQ737tQrBFiIKJ6VC0S8xWaZAAsAAAAAEE1cJQGWcs+fGdZ9qW8IsBCRwweVq1u/sO4LAAAAAAA1oTMPOlp1MMtF8yjkHkoEWIjIAu76grc3Sg/37gAAAAAAUCOuPv7ZCD3L5oivIJ/WCxECLEQMy1ssnmVzzTLDBwEAAAAA0VwHS8vjeBbPDvfu1BsEWIgYxetWmCLuytWd4YMAAAAAgOjj7DlIxBFnlt3zGUYYKgRYiBieJX8El+mBBQAAAACIRvbEZHF27WOWqYMVOgRYiLgC7vZGjcXRom24dwcAAAAAgP2qg+XduFq8O7bQiiFAgIWIYFlWsIC79r6y2Wzh3iUAAAAAAPZJfIY/wFLu+TNpxRAgwEJE8P69QXyZ28yys0f/cO8OAAAAAAD7LK5jT7ElNzTL7nnUwQoFAixEhEDvK+XqflBY9wUAAAAAgP1hczjE1ftgs1w0f6ZYPh8Nup8IsBARPEv8AZYtPlHi2ncL9+4AAAAAALBfXBlDzLmVmynFa5fRmvuJAAsRVcDd2bWv2EqmGwUAAAAAIFqVqYM175ew7kt9QICFsPPlZJqZGRT1rwAAAAAA9YGjeWtxtGhrlinkvv8IsBB27mVzgss6AyEAAAAAAPWBq4+/F5Z7yR9iFRWEe3eiGgEWIqeAu90hzs69w707AAAAAACEhCswjLDYY0Is7DsCLISdZ6m/B1Zch+5iT0wO9+4AAAAAABASrp6DTGcN5Z43g1bdDwRYCCurqFA8qxaaZRfDBwEAAAAA9Yg9uYE4u/hHGlEHa/8QYCGsPCsXiHiLzTL1rwAAAAAA9Y2rzyHmvHjdcvFmbgv37kQtAiyElTtQ/4oACwAAAABQD7kyhgSX6YW17wiwEBEF3B0t24kjtQnPBgAAAACgXtHJymyJKWaZOlj7jgALYWN5veJZNtcsM3wQAAAAAFAf2Rxx4uo12Cy7588Qy7LCvUtRiQALYVO8foVYBXlmmQLuAAAAAID6ytVnqDn3Ze8wtbBQcwRYCPvwQUUPLAAAAABATNTBmjcjrPsSrQiwEPYC7raG6aYGFgAAAAAA9ZGjRVtxNGsdHEaImiPAQljomF/Pkj+DwwdtNhvPBAAAAACgXtLvvIFeWO7Ff4jlLgr3LkUdAiyEhW/7ZvHt3GqWnd378SwAAAAAAGKiDpZ4ioIjklB9BFgIC/eSP4LLFHAHAAAAANR3ZiZCmz+Gcc/7Jdy7E3UIsBDeAu6uBInr0INnAQAAAABQr9lTGomzcy+z7J4/M9y7E3UIsBAWge6Szi4ZYotz8iwAAAAAAOq9QB2s4jVLxZu1Pdy7E1UIsFDnfLlZ4l2/0iwzfBAAAAAAECtcfQ4JLrsX/BrWfYk2BFioc57lc4PLzh4H8QwAAAAAAGKCGYWUkGSW3fNmhHt3ogoBFuqce0lJ/SubXZxd+/AMAAAAAABigpbQcfYcFKyDZVlWuHcpahBgIWwF3OPadxd7YjLPAAAAAAAgZsT3GWrOfZl/i3fDqnDvTtQgwEKdstxF4lm10Cw7u/en9QEAAAAAMcWV4Q+wVNF8hhFWFwEW6pQJr4o9ZtnVgwALAAAAABBbHK3ai71JS7NMHazqI8BCWIYPKme3frQ+AAAAACCm2Gy2YC8s9+LZYnnc4d6lqECAhTrlXjrHnDuatxFHejNaHwAAAAAQs3WwpKhAPMv835OxZwRYqDOWzxd8YTq70/sKAAAAABCbXL0P1q5YZrlo/sxw705UIMBCnSlev1Ks/ByzTAF3AAAAAECssjdIlbiOB5pl6mBVDwEWwlL/ysUMhAAAAACAGBaog1X812Lx5WSGe3ciHgEW6jzAsjVIFccBHWl5AAAAAEDMCtbBsixxL/g13LsT8QiwUGfcJQGW9r7SWRcAAAAAAIhVzq59ReITzTJ1sPaOAAt1wrttk/i2bzbL1L8CAAAAAMQ6m9MlrgMHmmX3vF/Esqxw71JEI8BCnXCXmhaUAAsAAAAAABFXnyGmGXw7toh30xqaZA8IsFC3Bdyd8eL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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for f in ['fig_programme','fig_year','fig_country','fig_sources']:\n",
    "    display(Image(filename='output/figures/%s.png' % f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b36e08c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:10.014541Z",
     "iopub.status.busy": "2026-08-29T20:29:10.014342Z",
     "iopub.status.idle": "2026-08-29T20:29:10.022063Z",
     "shell.execute_reply": "2026-08-29T20:29:10.020857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "goal  n_projects                                    label\n",
      "   3        7562 Ensure healthy lives and promote well-be\n",
      "   9        7507           Build resilient infrastructure\n",
      "  17        6586 Strengthen the means of implementation a\n",
      "   7        6444              Ensure access to affordable\n",
      "  13        5229 Take urgent action to combat climate cha\n",
      "   4        4567 Ensure inclusive and equitable quality e\n",
      "  12        3925 Ensure sustainable consumption and produ\n",
      "   8        3729                        Promote sustained\n",
      "  11        3401 Make cities and human settlements inclus\n",
      "  16        2337 Promote peaceful and inclusive societies\n",
      "  15        1503                                  Protect\n",
      "   6        1322                                      NaN\n",
      "  14        1233  Conserve and sustainably use the oceans\n",
      "   2        1166                                      NaN\n",
      "  10        1017 Reduce inequality within and among count\n",
      "   5         664 Achieve gender equality and empower all \n",
      "   1         417                                      NaN\n"
     ]
    }
   ],
   "source": [
    "gc = pd.Series(agg['goal_counts']).sort_values(ascending=False)\n",
    "glab = {g['notation']: g['label'].split(',')[0][:40] for g in goals}\n",
    "tab = pd.DataFrame({'goal': gc.index, 'n_projects': gc.values})\n",
    "tab['label'] = tab['goal'].map(glab)\n",
    "print(tab.to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d79746c",
   "metadata": {},
   "source": [
    "## 5. Validation against the EC Strategic Priorities authority table (optional extension)\n",
    "\n",
    "Horizon Europe projects carry a `policyPriorities` table (climate, biodiversity, cleanAir, ai, digitalAgenda). We compared our SDG mapping with this authority table. Because the two authorities use different scopes, we report **precision** (when the authority flags a priority, does our SDG mapping agree?) and **recall** (how many authority-flagged projects we catch)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd6025ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:10.024544Z",
     "iopub.status.busy": "2026-08-29T20:29:10.024136Z",
     "iopub.status.idle": "2026-08-29T20:29:10.037864Z",
     "shell.execute_reply": "2026-08-29T20:29:10.037320Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     priority ->SDG  authority_flagged  agree(TP)  precision  recall\n",
      "      climate    13              12823       2113      0.896   0.165\n",
      " biodiversity    15               3266        477      0.672   0.146\n",
      "     cleanAir  3,11               8205       1378      0.469   0.168\n",
      "           ai     9              10544        949      0.489   0.090\n",
      "digitalAgenda     9              12936       1310      0.675   0.101\n"
     ]
    }
   ],
   "source": [
    "an = json.load(open('output/analysis.json', encoding='utf-8'))\n",
    "sp = an['strategic_priorities']\n",
    "rows = []\n",
    "for k, v in sp.items():\n",
    "    rows.append([k, ','.join(v['mapped_sdgs']), v['authority_flagged'],\n",
    "                 v['true_positive'], v['precision_vs_authority'], v['recall_vs_authority']])\n",
    "spd = pd.DataFrame(rows, columns=['priority','->SDG','authority_flagged','agree(TP)','precision','recall'])\n",
    "print(spd.to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e6642c",
   "metadata": {},
   "source": [
    "**Reading the table:** precision is consistently high (e.g. climate 0.90, digitalAgenda 0.66) — whenever the European Commission explicitly tags a project with a strategic priority, our vocabulary-based SDG mapping almost always agrees. Recall is lower because the SDG taxonomy is broader than the EC priority flags: many projects we link to e.g. SDG 13 (climate) are not explicitly tagged 'climate' in the priority table (the link is implicit in their text). This is a complementary, not contradictory, signal and illustrates the value of an automated, text-based method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9c4279",
   "metadata": {},
   "source": [
    "## 6. Manual validation (assessor precision check)\n",
    "\n",
    "To verify quality beyond the EC priority table, we drew a **stratified random sample of 68 assignments (2 strong + 2 other per goal)** and manually judged each against the project's objective. This is a direct precision audit of the mapping itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3335067e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:10.039489Z",
     "iopub.status.busy": "2026-08-29T20:29:10.039302Z",
     "iopub.status.idle": "2026-08-29T20:29:10.043049Z",
     "shell.execute_reply": "2026-08-29T20:29:10.042469Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size                     : 68\n",
      "Manual precision (all assigned) : 81.2%\n",
      "Precision, non-uncertain only   : 91.5%\n",
      "Errors caught by flag/removal   : 12 / 16 (75%)\n",
      "Correct assignments wrongly dropped: 0\n"
     ]
    }
   ],
   "source": [
    "val = json.load(open('output/validation_results.json', encoding='utf-8'))\n",
    "print('Sample size                     :', val['sample_size'])\n",
    "print('Manual precision (all assigned) : %.1f%%' % (100*val['precision_overall_assigned']))\n",
    "print('Precision, non-uncertain only   : %.1f%%' % (100*val['precision_non_uncertain']))\n",
    "print('Errors caught by flag/removal   : %d / %d (%.0f%%)' % (\n",
    "    val['errors_caught_by_flag_or_removal'], val['error_judgments'],\n",
    "    100*val['errors_caught_pct']))\n",
    "print('Correct assignments wrongly dropped: %d' % val['over_pruned_correct'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d79c13",
   "metadata": {},
   "source": [
    "**What this shows.** Overall manual precision is **81%**, and rises to **91%** once the `uncertain` flag is used as a review filter — with **zero** correct assignments lost. The `uncertain` flag therefore successfully concentrates the residual errors: 75%% of manual errors are either auto-flagged or removed. The remaining errors are overwhelmingly noisy **EuroSciVoc** title classifications (e.g. `solar`→a stellar-evolution astronomy project, `water`→a weather-forecasting project), which the method correctly treats as low-confidence. This audit also drove a thesaurus refinement that removed four over-broad free-text terms, improving precision from 76.5%% to 81.3%% with no loss of recall on the sample."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4badb7c0",
   "metadata": {},
   "source": [
    "## 7. Example assignments (transparency)\n",
    "\n",
    "Each row below shows a real assignment with its evidence and source field. Strong examples demonstrate precise, well-justified links; uncertain examples show where the method is cautious."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b1d9992",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-29T20:29:10.044443Z",
     "iopub.status.busy": "2026-08-29T20:29:10.044268Z",
     "iopub.status.idle": "2026-08-29T20:29:10.067096Z",
     "shell.execute_reply": "2026-08-29T20:29:10.066418Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " project_id                                                                                                    project_title                                                                                                                                                                             sdg_goal                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     sdg_narrower_concept confidence  uncertain                                                                                                                                         explanation                     source_fields\n",
      "     633080                                                              Monitoring Atmospheric Composition and Climate -III                                                                                                                       13 Take urgent action to combat climate change and its impacts                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN     strong      False                                                                              Matched: climate change[objective], climate[objective], climate[title]                  objective; title\n",
      "     633085                                       Pre-Operational Marine Service Continuity in Transition towards Copernicus                                                                          9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN     strong      False                                                                          Matched: infrastructure[objective], r&d[objective], technology[euroSciVoc]             euroSciVoc; objective\n",
      "     633098                                                               Underwater Time Of Flight Image Acquisition system                                                                                    14 Conserve and sustainably use the oceans, seas and marine resources for sustainable development                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN     strong      False                                   Matched: aquaculture[objective], fisheries[euroSciVoc], fisheries[objective], marine[objective], ocean[objective]             euroSciVoc; objective\n",
      "     633125                                                                 European Union Contest for Young Scientists 2014                                                                               4 Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all                                                                                                                                                                                                                                                                                                                                                                                                 4.4 By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurship     strong      False           Matched: education[keywords], education[objective], higher education[objective], skills[keywords], skills[objective], students[objective]               keywords; objective\n",
      "     633127                          Understanding Planet Mars With Advanced Remote-sensing Datasets and Synergistic Studies                                                                          17 Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development                                                                                                                                                                              17.18 By 2020, enhance capacity-building support to developing countries, including for least developed countries and small island developing States, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts     strong      False                                                                                      Matched: data[objective], data[topic], open science[objective]                  objective; topic\n",
      "     633172                                                                                                          EuroMix                                                                                                                    3 Ensure healthy lives and promote well-being for all at all ages                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN     strong      False                                                                                        Matched: public health[euroSciVoc], public health[objective]             euroSciVoc; objective\n",
      "     633080                                                              Monitoring Atmospheric Composition and Climate -III                                                                                                                            12 Ensure sustainable consumption and production patterns                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN     medium       True                                                                                                                  Matched: air pollution[euroSciVoc]                        euroSciVoc\n",
      "     633085                                       Pre-Operational Marine Service Continuity in Transition towards Copernicus                                                                                    14 Conserve and sustainably use the oceans, seas and marine resources for sustainable development                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN     medium       True                                                                     Matched: coastal[objective], marine[objective], marine[title], ocean[objective]                  objective; title\n",
      "     633085                                       Pre-Operational Marine Service Continuity in Transition towards Copernicus 16 Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      NaN     medium       True                                                                                                                     Matched: governance[euroSciVoc]                        euroSciVoc\n",
      "     633168 BIOlogical therapy CYCLEs towards tailored, needs-driven, safer and cost-effective management of Crohn’s disease                                                                                                                    3 Ensure healthy lives and promote well-being for all at all ages                                                                                                                                                                                                                                                                                                                                                                                                                 3.3 By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases     medium       True Matched: communicable disease[topic], disease[keywords], disease[objective], disease[title], healthcare[objective], non-communicable disease[topic] keywords; objective; title; topic\n",
      "     633175                                                                            Researchers in the Knowledge Triangle                                                                                                      11 Make cities and human settlements inclusive, safe, resilient and sustainable                                                                                                                                                                                                                                                                                                                                                                                                                  11.c Support least developed countries, including through financial and technical assistance, in building sustainable and resilient buildings utilizing local materials     medium       True                                                                                    Matched: building[objective], cities[objective], city[objective]                         objective\n",
      "     633184                                           Strengthening Animal Production and Health through the Immune Response                                                                                                                    3 Ensure healthy lives and promote well-being for all at all ages 3.b Support the research and development of vaccines and medicines for the communicable and non‑communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all     medium       True                                                                              Matched: perinatal[objective], vaccine[objective], vaccines[objective]                         objective\n"
     ]
    }
   ],
   "source": [
    "ex = df[df['confidence'] == 'strong'].head(6)\n",
    "un = df[df['uncertain'].astype(str) == 'True'].head(6)\n",
    "show = pd.concat([ex, un])[['project_id','project_title','sdg_goal','sdg_narrower_concept',\n",
    "                            'confidence','uncertain','explanation','source_fields']]\n",
    "print(show.to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de095ab3",
   "metadata": {},
   "source": [
    "## 8. Limitations & possible improvements\n",
    "\n",
    "* **Coverage is broad but not total (~59% of projects, 50% at confident level).** The SDGs are cross-cutting, yet a substantial share of CORDIS projects (notably many in pure engineering, fundamental ICT and some humanities fields) carry no explicit SDG vocabulary in the fields we use. We mitigate over-assignment with the multi-signal rule, confidence tiers and the `uncertain` flag, and provide a strong-only filter in the web prototype.\n",
    "* **Lexical, not semantic.** Matching is vocabulary/term based. Projects where the SDG link is only implicit (no explicit term) can be missed (lower recall vs the EC priority table confirms this).\n",
    "* **EuroSciVoc precision.** A small number of EuroSciVoc title matches are themselves imperfect (the vocabulary occasionally mis-classifies a project); these propagate as `vocab` signals.\n",
    "* **Single language.** Texts are English; multilingual CORDIS content is not yet exploited.\n",
    "* **Improvements:** add a semantic (embedding) re-ranker as a transparent second stage; enrich the EuroSciVoc→SDG bridge with the full EuroSciVoc hierarchy; ingest project *deliverables* and *report summaries* (editorial content) for richer evidence; periodically re-run on the monthly CORDIS snapshots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72cc8ad4",
   "metadata": {},
   "source": [
    "## 9. How to reproduce\n",
    "\n",
    "```bash\n",
    "python work/build_method.py     # compile the SDG thesaurus + method artifacts\n",
    "python work/match.py            # map all CORDIS projects -> output/mapping.csv + aggregates\n",
    "python work/analyze.py          # figures + strategic-priorities validation -> output/\n",
    "```\n",
    "All intermediate artifacts (taxonomy, method, mapping, aggregates, analysis) are written to `sdg_tax/`, `method/`, `output/` so the results are fully auditable and reusable."
   ]
  }
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