diff --git a/nbs/word_steering.ipynb b/nbs/word_steering.ipynb
index cd1ca2e..d3c8ab4 100644
--- a/nbs/word_steering.ipynb
+++ b/nbs/word_steering.ipynb
@@ -37,17 +37,17 @@
    "id": "46973b3d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-11T02:45:16.413639Z",
-     "iopub.status.busy": "2026-07-11T02:45:16.413532Z",
-     "iopub.status.idle": "2026-07-11T02:46:35.799773Z",
-     "shell.execute_reply": "2026-07-11T02:46:35.799156Z"
+     "iopub.execute_input": "2026-07-11T13:10:51.541666Z",
+     "iopub.status.busy": "2026-07-11T13:10:51.541571Z",
+     "iopub.status.idle": "2026-07-11T13:11:01.438917Z",
+     "shell.execute_reply": "2026-07-11T13:11:01.438189Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e6a17e6831ae4273aa55aab600f54b29",
+       "model_id": "9418c87e483a48bf8c5c6b7d19dc44bf",
        "version_major": 2,
        "version_minor": 0
       },
@@ -61,7 +61,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7037a91991b24c60b7fa808554bbe3c5",
+       "model_id": "13d3927521cd460a9ff0e632049d853f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -75,7 +75,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ca6d6a1e569447efa5aa58f82daf7ed1",
+       "model_id": "136aee742a9540249c80e32a3060c6d9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -122,17 +122,17 @@
    "id": "19973ad5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-11T02:46:35.801594Z",
-     "iopub.status.busy": "2026-07-11T02:46:35.801238Z",
-     "iopub.status.idle": "2026-07-11T02:46:36.611288Z",
-     "shell.execute_reply": "2026-07-11T02:46:36.610667Z"
+     "iopub.execute_input": "2026-07-11T13:11:01.441111Z",
+     "iopub.status.busy": "2026-07-11T13:11:01.440792Z",
+     "iopub.status.idle": "2026-07-11T13:11:02.025861Z",
+     "shell.execute_reply": "2026-07-11T13:11:02.025342Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "76dde105569c4ae389c3ff1f373991b5",
+       "model_id": "a6d03e33bbeb4490904ee0d0d5197c80",
        "version_major": 2,
        "version_minor": 0
       },
@@ -146,7 +146,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ddcbdc53ec9044aa9ba37269194c45b6",
+       "model_id": "a4aef663151241578c26c7c8df5c1a77",
        "version_major": 2,
        "version_minor": 0
       },
@@ -197,10 +197,10 @@
    "id": "59ba3763",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-11T02:46:36.613189Z",
-     "iopub.status.busy": "2026-07-11T02:46:36.612958Z",
-     "iopub.status.idle": "2026-07-11T02:46:36.681499Z",
-     "shell.execute_reply": "2026-07-11T02:46:36.680913Z"
+     "iopub.execute_input": "2026-07-11T13:11:02.027027Z",
+     "iopub.status.busy": "2026-07-11T13:11:02.026900Z",
+     "iopub.status.idle": "2026-07-11T13:11:02.083802Z",
+     "shell.execute_reply": "2026-07-11T13:11:02.083334Z"
     }
    },
    "outputs": [
@@ -226,15 +226,18 @@
     "## Pick a coefficient: sweep the coherent window\n",
     "\n",
     "The raw coefficient is model- and lens-dependent, so don't hand-pick it: sweep OUTWARD\n",
-    "from C=0 in both directions and stop each side the moment the answer goes incoherent (the\n",
-    "rubric's `pmass` drops below 0.9). That keeps us inside the coherent range both ways and\n",
-    "traces the steered axis's dose-response, the expected rubric digit vs C colored by\n",
-    "coherence. SHOULD: ans sits ~5 at C=0 (neutral) and climbs toward 9 as +C adds joy; -C\n",
-    "drops it to a low floor (~2 on this prompt, a step then a noisy plateau, not a smooth\n",
-    "glide to 0), with pmass~1 across the coherent span and dropping (red edge) at the ends.\n",
-    "Coherence here is the ANSWER slot's, which is more robust than free-form text: the\n",
-    "qualitative `show_steer` below shows the text itself already frays around C~0.4 on this\n",
-    "prompt even while the forced digit stays clean.</cell id=\"0717a9b9\">"
+    "from C=0 in both directions and stop each side the moment the model can no longer emit a\n",
+    "valid answer object. We ask for one line of JSON, `{\"ans\": N, \"why\": \"...\", \"2+2\": M}`,\n",
+    "and gate coherence on it parsing AND `2+2 == 4` (a trivial-arithmetic canary a steer-fried\n",
+    "model flubs). That keeps us inside the coherent range both ways and traces the steered\n",
+    "axis's dose-response: the expected rubric digit vs C, colored by the valid-object fraction.\n",
+    "SHOULD: ans sits ~5-6 at C=0 (neutral) and climbs toward 9 as +C adds joy; -C drops it\n",
+    "toward a low floor; the object stays valid across the coherent span and breaks (red edge)\n",
+    "at the ends. We gate on the object, NOT on the model's confidence, because a steer-fried\n",
+    "model collapses into a CONFIDENT degenerate loop: its top-token probability climbs back\n",
+    "toward 1 on repeated garbage, so confidence is not coherence, and the valid-object\n",
+    "fraction can't be fooled by confident junk. The qualitative `show_steer` below shows the\n",
+    "free-form text frays earlier than the short forced object."
    ]
   },
   {
@@ -243,10 +246,10 @@
    "id": "c271f279",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-11T02:46:36.682741Z",
-     "iopub.status.busy": "2026-07-11T02:46:36.682591Z",
-     "iopub.status.idle": "2026-07-11T03:00:23.914272Z",
-     "shell.execute_reply": "2026-07-11T03:00:23.913812Z"
+     "iopub.execute_input": "2026-07-11T13:11:02.084792Z",
+     "iopub.status.busy": "2026-07-11T13:11:02.084674Z",
+     "iopub.status.idle": "2026-07-11T13:16:05.922155Z",
+     "shell.execute_reply": "2026-07-11T13:16:05.921763Z"
     }
    },
    "outputs": [
@@ -254,31 +257,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|     C |   ans |   ans_std |   pmass | coherent   |\n",
-      "|-------|-------|-----------|---------|------------|\n",
-      "| -0.90 | +3.30 |     +0.45 |   +0.83 | False      |\n",
-      "| -0.80 | +2.58 |     +1.06 |   +0.98 | True       |\n",
-      "| -0.70 | +2.59 |     +0.45 |   +1.00 | True       |\n",
-      "| -0.60 | +2.17 |     +1.31 |   +1.00 | True       |\n",
-      "| -0.50 | +2.82 |     +0.51 |   +1.00 | True       |\n",
-      "| -0.40 | +2.22 |     +1.47 |   +1.00 | True       |\n",
-      "| -0.30 | +2.27 |     +1.91 |   +1.00 | True       |\n",
-      "| -0.20 | +4.94 |     +0.06 |   +1.00 | True       |\n",
-      "| -0.10 | +4.90 |     +0.10 |   +1.00 | True       |\n",
-      "| +0.00 | +5.06 |     +0.09 |   +1.00 | True       |\n",
-      "| +0.10 | +6.66 |     +0.59 |   +1.00 | True       |\n",
-      "| +0.20 | +7.11 |     +0.17 |   +1.00 | True       |\n",
-      "| +0.30 | +7.80 |     +0.37 |   +1.00 | True       |\n",
-      "| +0.40 | +8.43 |     +0.36 |   +1.00 | True       |\n",
-      "| +0.50 | +7.99 |     +0.48 |   +1.00 | True       |\n",
-      "| +0.60 | +7.97 |     +0.12 |   +0.99 | True       |\n",
-      "| +0.70 | +7.93 |     +0.23 |   +0.96 | True       |\n",
-      "| +0.80 | +7.46 |     +0.33 |   +0.89 | False      |\n"
+      "|     C |   ans |   ans_std |   span_pmass |   valid_frac | coherent   |\n",
+      "|-------|-------|-----------|--------------|--------------|------------|\n",
+      "| -0.50 | +2.29 |     +0.98 |        +0.91 |        +0.00 | False      |\n",
+      "| -0.25 | +4.43 |     +0.40 |        +0.93 |        +1.00 | True       |\n",
+      "| +0.00 | +5.98 |     +0.71 |        +0.96 |        +1.00 | True       |\n",
+      "| +0.25 | +7.03 |     +0.74 |        +0.96 |        +1.00 | True       |\n",
+      "| +0.50 | +7.97 |     +0.21 |        +0.84 |        +0.67 | True       |\n",
+      "| +0.75 | +7.68 |     +0.19 |        +0.73 |        +0.00 | False      |\n"
      ]
     },
     {
      "data": {
-      "image/png": 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8udTry8vLI0uXLiV2dnZEWVmZGBkZEW9vb/Ljxw+5rpMQQl69ekXatWtH1NTUhEO9Squ7tHWQJl9J790///xDVFRUyPDhw0V6CJekS5cuBAAZNGiQ2L6EhASyYsUK0rx5c6Kurk6MjY1J3759ycOHD0stU9rjSnv/CCHk5MmTpGPHjkRLS4toamqSli1bkuXLl4t8gZImX2UGZEKKbgymTp1KDAwMiKamJhkwYACJiYkhNjY2ZMSIEaW+VwIFBQXk0KFDpHv37kRfX5+oqKgQa2tr4ubmRk6dOkXy8/OlKodSLAYh5extUke0bdsWenp6Uk1VKUtegYcPH8LV1RWLFy/G+vXrRfZZW1tjzJgxWLdundhxvXr1Qm5urkhPaVmVdm6KoqoeIQRqamqYNm0aduzYoejqUFWEtiFXE+fPnweDwRBrh3v79i2ioqJKbD/Ozs4W6+RUUeemKEoxrl27hoKCAnTr1k3RVaGqEJ3Luhp49+4dTp8+DS8vLzg4OIjsa926tcQhM4QQBAcHIzg4uFxL6JV2boqiKp9gdEPfvn2hra2NR48eYfr06XBycsKgQYMUXT2qCtGArGAMBgPq6upwdXXFrl27pD5u9OjRuHfvHry8vDBnzpwqPTdFURXH09MTK1euxMaNGxETEwNjY2MMHToUa9asAYtFP6IBIDk5Gf7+/jA1NYWbm5tUx3z79g0vXryArq6ucE0BefJUJdqGTFEURVVL2dnZmDZtGh4+fAgmk4mWLVvi+vXrZR63detWrFixAj169EBUVBTS09Nx//59kdkbpclT1WhApiiKoqqltLQ03Lx5E56enhg/fjxycnLKDMifPn1Cs2bNcP78eQwfPhw8Hg99+vQBIUQ4daw0eRSBduqiKIqiqqV69erB29tbbPKm0ly4cAEmJiYYNmwYAEBJSQkzZsxAQECAcLpUafIoQo1voODz+YiLixNOe0lRFEWVjBCC7OxsmJubg8msuHuy/Px8selGS6tD8c9rVVVVmQJvST5+/IgmTZqIlN+0aVMQQhAaGgpTU1Op8ihCjQ/IcXFxsLKyUnQ1KIqiapTo6GhYWlpWSFn5+fmwtdZCQhJPqvxaWlrIyckRSVu5ciVWrVpV7rpkZWWJLdYhmOM9KytL6jyKUOMDsra2NoCiXy7Bsm8UVRPweDxERkbC1tYWSkpKiq4OVUdkZWXByspK+NlZEQoKCpCQxEPka2voaJd+152VzYet0w+xz+yKuDsGihbNKb7Ai+C1urq61HkUocYHZMEjBx0dHRqQqRqFx+NBS0sLOjo6NCBTVa4ymvg0tYq20vD+vxtxZX1mOzg4CNe6FhCs4CeYa0GaPIpAO3VRFEVRFYIPItVWkTIzM3H48GHExMQAKFrFKywsDG/evBHm+fvvv9GkSRPY29tLnUcRavwdMkVRFFU9FBIeCssYSVtI+DKVefr0aeTn5yMiIgIcDgeHDx+GsrIyxo8fDwCIj4/H5MmT4e/vD0tLS3Tr1g0jR47E4MGDMX36dERERODUqVO4efOmsExp8igCDcgURVFUhZDmDljWO+TXr18jOzsbLVq0AAA8f/4campqwoCsp6eHiRMninTuPXPmDM6dO4dnz57BxMQEb9++RdOmTUXKlSZPVavxE4NkZWVBV1cXmZmZtA2ZqlF4PB7Cw8Nhb29P25CpKlMZn5mCMiM/m0G7jE5d2dl82DaOp5/ZEtA7ZIqiKKpCVMYdcl1CAzJFURRVIQoJkaINueYH5JycHNy5cwdPnjxBfHw8WCwWbGxs0L17d3Tr1k3uJ160lzVFURRVIXggUm01VVpaGubPnw8LCwuMGzcOz58/R15eHtLS0nD9+nX06dMHtra22LZtGwoLC2Uun94hUxRVrbDZbLDZbLF0DQ0NhS+PR5WOR/4bZ1xanpooLS0Njo6OcHNzg6+vL7p06SJ2J8xms3H9+nUcPHgQFy5cwLNnz2Q6Bw3IFEVVK6GhoSLjQwXatGmDtm3bKqBGlLT4/7+VlacmUlVVxb///ouGDRuWmEdDQwMjRozAiBEjJP4Ol4UGZIqiqhVHR0fY2NggPT0dAQEBcHV1hb6+Pr07rgG4hIFCUvoMYNwy9ldXmpqapQZjQHTRjDZt2sh8DtqGTFFUtaKhoQFdXV3k5eUJh9MYGhrSgFwD8MCQaqsN3rx5g927dwtfT58+HWpqanBxcZF7CUcakCmKqjby8vKwZ88e9O3rDh8fH5w7dw6TJk3EhQsXwOfX1IeddUddCshTp05F586dAQBPnz7FhQsXcPLkSRgbG+OPP/6Qq0z6yJqiqGohPz8fM2bMwIcPH2BlxseAHoXI5zBw93E8Nm3ahLCwMCxdupSue16N8QkD/DIeSZe1vybIycnBt2/f0KpVKwDAzZs3MW7cOHh5eaFx48YYN26cXOXSO2SKoqqFU6dO4cOHDxg1mIN/DuTgVx8OFk7Ph9/RLDi35uLq1at4+vSpoqtJlaIASlJtNR2LxUJ+fj7y8vIAALdv30bPnj2F++X90kjvkCmKUjg+n48rVy7B2JBgtg8HzJ9uFdTVgP/NzsPQSdq4ePEiOnXqJFWZdPhU1SNS3CGTWnCHrKamhrZt28LT0xPW1tYIDw9Hjx49AAD//vsvunXrJle5NCBTFKVwmZmZSEpKwZA+hWBJuIEyMyZoYMvDt29fpS6TDp+qetK0EdeWNuS///4by5cvR1RUFK5evQoNDQ3w+XzcuXMHBw8elKtMGpApilI4ZWVlAEB2ruQPa0KArBwGlFVVpC6TDp+qejzCBI+U3hJaUycGKc7KygrHjx8XSWMwGLh27ZrcZdI2ZIqiFE5LSwstWrTA4yBlpKSJB+Wgd0qIT2SiVatW4PF4UpWpoaEBQ0ND6OvrAwD09fXp8KlKVggmCqFUxlY7wg4d9kRRVK3E5XLRq1cvFBQAc1Zq4NO3oo8mPh94HMTCiq2aUFZmoV27dvj48SOSk5NRw1eOrZUEd8hlbbUBHfZEUVStk5OTg4iICDRt2hQ+Pj44evQoxs/VgqkRQT4HyMhiQFVVBRs2rEeHDh0QFxeHqKgoJCYmwtzcHPr6+nQoVDXBBwP8MtqIy9pfE1TWsCeFB+Rnz57B398f6enpqF+/PsaMGQNzc3NFV4uiKBnJ2quZEILExETExsZCU1MTdnZ2aNmyJXr06IGLFy/iw4cP4GVlYcQIV4wdOxZmZmYAAFtbW5iYmCA2NhaRkZFITEyEhYWF2GL3giXyPn36hMjISDRt2hSGhoYyXxchBGFhYUhISICOjg6aN28OFkvhH53VEh9M8Mp48Fob1kP+ediTuro6bt++jZUrVwr318hhT9u3b8fixYsxc+ZMNGzYEHfu3MEff/yBp0+fonnz5oqsGkVRMpKlVzOXy8X379+RmZkJExMTWFhYCD/EGjdujGXLliElJQWXL1+Gh4eHWCDV0NBAgwYNkJ2djdjYWHz9+hU6OjqwsLCAhoYGbty4gY0bN4DNzhMeM2fOHLRt2xYbNmyAnp6eVNf06tUrbNu2DV++fBGmmZqaYNKkyRgyZIiU70zRl5Xs7GwEBwcjMzMTBgYGaNq0KbS0tGpVm3YhYaGQlD7OuKy5rmuCWjnsae/evZg5cya2bt0KAJg1axYaNGiA06dPY+PGjYqsGkVRMpK2V3Nubi4iIiLA4/Fgb28vdXCURFtbG40aNUJGRgZiY2Px6dMnhIeHY9OmTTA2JJgzMQ/tWxciNZ2JS9dV4R/wCnPnzsHhw0fKvMsNCgrCr7/OhpISD+7uuWjcqABxcSzcucvHmjVrkJaWBh8fH6nqefjwYVy6dBG5uf89QdDW0caokaMwZcoUua+/uuERBnhlBNyy9tcUtW7Yk6WlJZKTk4Wv2Ww2cnJyYGVlpcBaURQlj+KPpgW9mgUIIUhOTkZMTAzU1dXRsGFDqKqqipUjePSdnp4OAMJ/S3r0zWAwoK+vDz09PaSkpGDt2jVQVwMObMqCmXHR41EzYx6aNWJDS4vgn2shCAwMRPfu3Uu8FkIINm7cAGVlLjZuSIaNDVe4b9CgHCxabIQDB/ZjyJAhqFevXqnvyz///IOTJ09CQ5cBJw8W6lkykfKdj9D7OTh48CCsra3Rp0+fUsuoKXhSPLLm1YJH1oDkYU9MJrNcw54UGpCPHz+OadOmoUuXLrC2tsarV68wadIkTJs2rcRjOBwOOByO8HVWVlZVVJWiqDLk5eXh9u3bePLkCaKjo8FisTBixAhoaWmBx+Ph+/fvyMjIgLGxMSwsLMBkSv7gLv7oOyAgAEDZE3owGAzk5OQgKioaQ9w5wmD8s3HD83Hxuipu3LiB9u3bCx+TMxgM4QYA79+/x48fURgyJFckGAOAjg7BmNFZ2LS5Hm7cuIGxY8eWWKecnBzs2LkDOsZMDF2tAnXdovLtOyjBsacSrqwowNatW9CjRw/hWOyajE+Y4JfRi5pPe8eXSKEB+eXLl3j9+jVGjhwJe3t7ZGRkwM/PD5MnT4a1tbXEY9avX4/Vq1dXcU0piirNu3fv8PvvvyEjI1OY9u3bN5w4fgwrVq6CiYkJuFwu7OzshOOCSyJ49F2cNG2tmZlF57e2lLwylJEBgaYGEBcXh0+fPpVYzrNnzwAAzZtxJO5v1qwAAPDx40d8+/YNTCYTSkpKIhuTycTt27eRn5ePDmOVhcFYQNuIieZ9lfDiXDoCAwPh6upa5vVVd4VgoqCsNuRacocMABcvXoS/vz9iYmJExscbGRnh7NmzMpensAFh+fn58PHxwe+//44dO3bg119/xfXr16GtrY3FixeXeNySJUuQmZkp3KKjo6uw1hRFFRcbG4s5c35FAScTv03Nw71zWQi4kIWlv+aBxcrDsqX/Q0xMDJo0aVJmMAb+m9Cj+CZNQDY2NgYAfPoqOSj8iGEiJ7eop3bjxo3RuHFjNGrUCA0bNkSDBg3QoEEDODg4CL8QJCVJLic5uShdS0sLAFBYWAg2m42MjAwkJSUhJiYGP378EAZ900aSP2oF6XFxcWVeW03AB1OqrTZYtWoVZs2aBS6Xi2fPnqFJkyZISUnBgwcPJH6hlIbC7pBTUlKQlZWFFi1aiKS3aNECr169KvE4VVVVie1OFEUpxrlz55Cby8bW5Wx0cf7v8e5gt0LY1edj4u+aCAwMhLu7e6XXxdzcHE5OTrgf+BojB3Pg2PC/uxYeD9h/Ug0AMHToUGhqapZYTs+ePbFly2bc9OfCzY2Nn58mEwL4+hUd6+HhAQcHB4llEELw/v17AEBWEoGemXie7KSiu0VBYK/ppJs6s3YE5G3btuHJkyfQ09PD/fv3sWvXLhBCMGXKFOEqULJS2DtjYWEBQ0NDkQZwNpuN+/fvo2XLloqqFkVRMrp//x6szPjo3J4rtq95Yx5aNOHiwYP74PMlP0auaLNnzwaLpYyZ/9PGziNqePGWhZv3lTF1oRYCnqrA1dUVbdq0KbUMTU1NjBo1GtHRLKxcZYDQUBXweEBMjBJ27tTDv/9qwNGxCZo2bVpiGQwGA25ubmAwGHh/gwvCF31Uy+MSfLjFA4vFQosWLWrFzGOCiUHK2mq6pKQkKCsro1mzZlBVVUVubi6Aop/57Nmzce/ePbnKVdgdMoPBwNGjRzF27Fi8evUKdnZ2CAwMhI6ODtasWaOoalEUJaPc3FxYO/BR0lwIpsYEwZ8KweVyoaIi/eIQ8mrWrBl27dqDtWvX4O8rUfj7SlG6MouF4cOHYP78+VJN3DBlyhRkZmbi4sWLWLxE9Klcy5YtsW7dujLLsLS0RN++fXHz5k3c+qsAbYYoo54lA8mRfLy6xEVyJB/9+/dFWloa8vPzYWVlVerdcnVfUrKAsKBESg8rBTX/ewcKCgqgplb0tMXQ0BD5+fmIjo6GlZUV0tLS5P5ypdBOXQMHDkRkZCRevHiB1NRUzJgxAx07diyx9yVFUdWPpaUlPn0LQ15+0drFP+PygPehLBgbG1ZJMBZo06YNLl68hNevXyMkJAQfPnzAtGnTSny8LImSkhIWL14MT09PXLt2DfHx8dDR0UGfPn3g5OQk9WxMS5cuRV5eHgICAvDjjWgnsQEDBmDZsmXIy8tDTEwMvnz5Aj09PVhaWkpsmqvuS0rypVgPuaz9NQ2DwcCIESPQt29f9OrVC5cuXcKIESPkKkvh87/p6+tXSdsSRVGVY/DgIdi0aRP2n1LF3EkckTvlM5dVkJjMwOTJQ6u8XgwGA23btoWNjQ04HI7cE5DY29tj7ty5ctdDVVUVXl5e0NfXx7dv35CXlwdNTU00bNgQPXr0AIvFgra2Nho3boy0tDTExsYiJCQERkZGMDMzE5nAxNHREbb16qHg2jWEvX4Nezc3qLm5QaOatEFLN3Vmzb/hMjU1xf3794Wv9+/fj3Xr1uHDhw+YOnUqFixYIFe5Cg/IFEXVXAUFBWjUqBHs7Gxx1jcSIWEsuLsWgMUE7j5Wxsv3LNja2mL06NGKrqpCSTOUi8FgwMDAAPr6+khMTERCQgJSU1NhZmYGIyMjMAmBxpo1UNu5E8zcXJgBwKlT4DdoAObevUCvXlV2PSWRbhxyzQ/ILBYLjRo1Er5WV1fHn3/+Wf5yy10CRVF1Um5uLr59+wYGg4E9e/Zi//798Pe/ieBPRUOClJSYcHPrhQULFkBbW1vBtVUsWdp4mUwmzMzMYGhoiLi4OMTExCApKQkNt26F6okTCLM0xclhboirp4sOXyIw9mEQNPr1A+P2bUDBY5kLiRKUypzLWvb21ejoaAQFBUFXVxddunSRaqRNcHAwvn37BmNjY7i4uEBJ6b965eTk4OrVq2LH9OzZU7iIiSLQgExRlMxSU1Px48cPaGhowN7eHsrKyli+fDlmz56NJ0+e4OnTpxg3bpzIXQQlG2VlZVhbW8PY2BhJ9+9D9cQJPGlijwnzf0Hh/z/GftiyMa64tIHvmj1Q/f13MF69Qom966oADwCvjF7UvFL3itu9ezcWLVqEjh07Ijo6GoWFhbh37x5sbW0l5mez2RgyZAjev3+Pjh074vPnz8IVmYyMjAAACQkJGDt2LIYMGSIy/K1FixZSB+SoqCisWrUKr169Epsx0szMTDi5jCxoQKYoSmqEEMTFxSEhIQEGBgaoX7++SCdMPT09ODs7IzY2FgYGBgqsqexzYldX6urqsH7wAACwZVgfYTAW+GJliksd22DMwxdAcDCgwGGjFf3IOiwsDHPnzsWpU6cwatQocLlc9OrVC9OmTcPt27clHrN27Vp8/PgRoaGhMDAwAI/Hw8CBAzFv3jycPn1aJO+2bdvknsRj0KBB0NbWho+Pj1jP+NLGuJeGBmSKoqTC4/EQGRmJzMxMWFpawtjYWKSncXULgPLOiV0tRUUBAN7ZWkrc/dbeqiggR0UpNCBX9MQg58+fh6GhIby8vAAUtd3OmjULI0aMQFJSknBmtp8FBQWhW7duwi+ESkpKGDx4MObMmYODBw+K/C4+fvwYb9++RcOGDUsdU15ceno6wsLCkJaWJhz+VBFoQKYoqkwcDgffvn1DQUEBHBwcoKurK5anugXA8syJXe38/4pStompiDAzEtttm5BS9B8FP5XgEqUy10PmkqIJYoo/5pU0C+OHDx/QtGlTkacwzZs3ByEEISEhEgOyhYUF3r9/D0KI8AtjaGgoOBwOwsLC0KpVKwBFgXrfvn3Q19dHYGAgOnTogHPnzkk1vaumpiZUVVVF2qUrAg3IFEWVKjs7G+Hh4WCxWGjcuDHU1dUl5qtuAbCmPZou1ahRwKFDmH7zIRb4DBdpJ66XlYNRj1+Bb2MDprOzAisp2zjk4svsrly5EqtWrRJJy8zMFAuQguUuBQuJFLdw4UI4OztjyJAh6N+/P0JCQnDjxg2RY3R1dfHq1SthcE5ISECHDh0wf/58HDt2rMzrVFFRwfjx4zFv3jxs3LhR7kfUxdGATFF1XEhICM6dO4egoBcoKCiAra0dBgwYADc3N+Tl5SEqKgra2tqws7MTGRNbXK0KgNVN9+4grq7wDAiAaiEXR3t3QqyBHlw+RWCu333Uy8oBdu4GKviOTVbSrYdctD86Oho6OjrCdEk9p9XV1ZGTkyOSlp2dLdwniaOjIz5//oxjx44hKCgIdnZ2OHjwIHr27Cls6zUyMhJ28AKKxhVPnz4dW7ZskeIqi4wYMQKurq7Cu+yfmZubIzg4WOqyBGhApqgaqiKmUXzw4AGWLFkMHo8Pu/o86JgShIQGIzg4GNevX8f06dNhYmICKysrqWemoioBgwHGlSsgI0di0K1bGPTivXAXUVUFtm8Hxo9XXP3+nyx3yDo6OiIBWRI7Ozv4+/uLpP348UO4ryQWFhZYtmyZ8PWOHTugqqoKR0fHEo/R1NREWloa+Hx+mbNF8vl8eHl5oVu3bsI1v38m7xdTGpApqoYq7zSKCQkJWLZsKfR0+Fi/OBetmhYNSEnPZGDDHjUEPA3Gy5cvMWPGjAqvOyUHXV0w/P2B16/BPnkSkcHBsO7ZE1rTpgGGhoquHYCiccjMMschS7/ISP/+/bFt2zYEBwcLVwY8d+6ccLlMoKgt2s/PTziGmM1mg8/nC4Mkm83G3r17MXbsWOFddWxsLCwsLITnIYTg8uXLaNeunVRTN6elpSEtLQ3Xr1+v0ClhaUCmqBpK0Gabnp6OgIAAuLq6Ql9fX+pv55cuXUJBQSGWLmELgzEA6OsS/LkgD0MnsXD9+jVMmTKl1EfVVBVzcgLb2hpPLl+GiYcHtKpJMAYqfi7rnj17wsPDA4MHD8bs2bMRERGBo0ePws/PT5gnLi4OY8eOhb+/P8zMzJCbm4s+ffpgxIgR0NTUxJEjR6CtrY3NmzcLjzlz5gzu3LkDNzc3qKur49KlSwgODsbNmzelqpeBgQHU1NQqfIWumj+HGUXVURoaGjA0NBS2X+nr68PQ0FDqgPz69Wvo6gAdncSXTVRRBnp1LkBSUjJiYmIqtN6U/NhsNlJSUkSGlqWkpEhsulAE8v/jkEvbiIxTZ164cAHLly/Hp0+foKqqiqCgIPTt21e4X1dXF2PGjIG5uTmAovbhq1evIicnBx8/fsTcuXPx9OlTkbnMFy5ciD///BMZGRn4/Pkzhg4divDwcHTo0EGqOjEYDIwdOxYzZsxAWlqaTNdTGvq1l6LqKB6PByUmKXFiJ2Xlon+rah1jqmzVbWhZcTwwpJipS7a+CEpKSvDx8YGPj4/E/WZmZmITftSvX7/MZXxdXFzg4uIiU10E4uLicOjQIbDZbBw9elSsl7WFhQW+fPkic7k0IFNUHdW4cWOEhITgXYgSWjcTndCQxwMCnihDT08XlpaSJ6Ogql51G1pWHJfPBJNfxjhkvqyTZ1Y/9erVw5kzZ0rcX1IP8LLIFZB5PB4+fPggfJRlZWWF5s2b03WMKaoGKCgoQFxcHJo3b47Lly9h3W51bF+VCwvTovawgkJgx2E1RMcz8csvHlW6jjFVuuo+tIwPBvhl3AGXtb8mUFNTw5AhQyq8XJkCckhICHbs2IELFy6IDcrW09ODl5cXfv3111K7llMUpRg8Hg8JCQlITEyEkpIS2rdvj99/X4DNmzdj+BRttGvFha42QdA7FtIzGWjZsiUmTpyo6GpTNQiPMMAro9NWWftrkry8PPj6+uLr169QVVVFixYt0KdPH7mHCEodkKdPn44zZ85gyJAhOHjwINq1awdTU1MQQpCYmIigoCBcv34dHTp0gLe3N/bu3StXhSiKkk1GRgaysrKQn58vcT+fz0dKSgri4+PB4/FgYmICU1NTKCkpwcvLC4WFhbh06RKev4kGAOjoaMPZ2RHe3t4VOk8vVfvVlfWQAeDJkyfw9PREdnY27O3tUVBQgG/fvqFFixa4du2aXMs4Sh2QdXR08P37d+G0ZT+ztbWFra0tvLy8kJaWho0bN8pcEYqiZPPixQscPXoUr1+/BgBcuXIZ7u59MWXKFJiYmIAQgoyMDMTGxoLD4cDAwADm5uZij6A9PDzg7u4OHo8HLpcLFRUVMBiMav1olKqeeGCCW9biErVgcA8hBN7e3vDw8BCZOjM+Ph5jxozB/PnzcfbsWZnLZZCKHkhVxbKysqCrq4vMzMwyZ32hqOqEx+MhPDwc9vb2Mk9Sf+PGDaxatQosJYKuHQphZszH2xAWQr4owcjIADt37gaPx0Nubi50dHRgaWkpd0cTqnapjM9MQZnD7o2HsmbpfQ4KcwtwqdeJGv2ZnZKSgvr16yMzMxPKguEI/+/169cYOXIkvn79KnO5tJc1RdUw6enpWLduLYwN+di3LgeWZoLv1BzcCmBhxdaiNWEXLlyIBg0a1NgPParmqSuPrAUTg+Tn54sF5KysLFhbW8tVrszvzJs3b+Dt7Q0bGxuoqalBXV0dzZo1w7Jly8SW06IoquJdu3YNHE4BZk/I+ykYF3F35aK7SyE+fvwo1VzBFFWRBDN1lbXVdAwGA9OnT8fw4cPx8eNH8Hg8FBQUICAgANOnT8ecOXPkKlemgHzr1i106tQJqampcHR0BJ/Px5w5c9CxY0ccOnQITk5OSEhIkKsiFEVJJywsDADQxVl8hi0A6Pr/6d++fauyOlEUAHAJU6qtpouLi8P27dtx584dNG/eHKqqqlBTU0OPHj0QFhaGUaNGQUtLC1paWmjUqJHU5cr0yHrx4sU4evQoRo0aBQA4e/YsTp06hZs3b2L79u0YOXIklixZItV6khRFyUfwiCyXzYC6mngXkFx20R0InX+aqmoVPZd1dVXWxCA/k6Xvhkx/sZ8/f8bgwYOFrwcNGiSczkxDQwM7duxA+/btZSmSoigZubi44Nq1a/C9rYyJowpE9vF4gN9dZairqwkXX6eoqlJXAnJlTQwi07MDU1NTfPr0Sfj606dPIos8m5iY0HlvKaqSubq6wtzcDIfPqeHiDWVw/j8mJ6UwsPIvdXyNVMLQoR5ia7RSVGWrzW3IKSkp2L59OzgcjlR5N2zYIPM5ZArIXl5eGD58OHbu3ImdO3fC09MTHh4ewv1nzpzByJEjZa4ERVHSU1ZWxvbtO6CnVw+b9qmj71gdeE7VxGAfbdx5pIwuXbpg1qxZiq4mVQfxCKPM9uOaOlOXhoYGHj58CBsbGyxatAgPHz4UzljJ5/MRHx+PK1euYNy4cXBwcJCrk7NMj6xXrlyJtLQ0LFu2DIQQDBs2TGRFDV1dXbm+FVAUJRsbGxusXv0HgoKCEBQUhMTERLRt1xCenp7o2rUrnVeeUoja/MhaQ0MDV69excOHD7Ft2zZs27YNhYWFUFZWBo/HA5/Ph46ODkaNGoUXL17I1JlLQK6JQQSHyDtfZ0WiE4NQNVV5JgZJTk5GVFQUHB0dkZubi8uXL8PDwwOG1Wixeqp6qsyJQbpemwGWpmqpebm5HPw7cG+N/8zOzc3F69evER8fDxaLBWtra7Ru3Vrmv+WfydUNs3gg3rJlC37//Xe5K0FRlPQEj8fq1asHdXV15ObmKrpKFAWgdt8hF6epqYmuXbtWaJkV8lxrwYIFFVEMRVFSSEpKQmFhIczNzRVdFYoSwSNMqTZKMjpQkaJqEMESioaGhlBVLf3RIEVVtbp0h1wZFP5VJTU1FQsXLkTbtm3RtWtXnD59WtFVoqhqKzExEXw+X66l3SiqshHCkGqjJKuQO+THjx/LdVxqaiqcnZ1hZ2eHbdu2QUNDA7t374aNjQ06d+5cEVWjqFqjsLAQiYmJMDY2FltCkaKqA3qHXD7lCsj5+fkghMgdPJcvXw4ejwc/Pz/hQujHjh0Dj8crT7UoqlYSzBNvamoKAGCz2WCz2UhPTwcA4b8aGhp0LWNKIfh8Jnj8MlZ7KmN/TcLj8fD582ckJiaiR48e5S5P5nfm3r176Nu3L3R1daGurg4NDQ3o6emhb9++uHfvntTlEEJw/vx5eHt7C4OxQHm6jVNUbVRQUIDk5GSYmJgI56gODQ3F5cuXERAQAAAICAjA5cuXERoaqsiqUnUYAUBIGZuiK1lBvn37hjZt2qBZs2YYN24cgKIREK1bt5b7b1CmO+QzZ85g/PjxcHNzw/Lly2FkZAQGg4GkpCTcv38f7u7uOHHiBMaMGVNmWSkpKUhLS4OVlRUmTJiA169fw9zcHOPHj8fo0aNLPI7D4YhMXUaXfKTqgvj4eDCZTJiYmAjTHB0dYWNjI5aX3h1TisIHAwyU8ci6jP01xaRJk9C1a1ecP38evXr1AgAwmUz8/vvv2LBhA06ePClzmTIF5NWrV+PgwYPCBSV+9vvvv+Po0aP4448/pArIBQVFE/AuWLAA69evx/z58/HixQv4+PggMzMT06dPl3jc+vXrsXr1almqTVE1Wn5+PlJSUmBpaSny9Ig+mqaqG2k6bdWGTl35+fl49eoV7t69i6SkJJF9rVu3xqpVq+QqV6ZH1j9+/MDQoUNL3D906FB8//5dqrIMDAzAZDIxdOhQzJgxAy1atMDkyZMxYcIEHDp0qMTjlixZgszMTOEWHR0tyyVQVI0THx8PZWVlkYVcKKo64vEZUm01HZvNBoPBgLKysthEWampqXIPSZQpINvb25e6BuTp06fh4OAgVVlqampo0aIF9PT0RNL19PRKnXlIVVUVOjo6IhtF1VZ5eXlIS0uDmZkZnZ+aqvbqyrCnevXqwcLCAr6+viIBOS8vD3/++afcM3jJ9Mh67dq1GDFiBM6fPw9XV1fhN/bk5GQ8fPgQz549wz///CN1ebNmzcLSpUsxa9YsODg4IDIyEqdPn4aXl5dsV0FRtVRsbCxUVFToHNVUjVBXHlkDwI4dOzB8+HC4uroiKysLkyZNwu3bt8Hj8XDs2DG5ypQpIA8dOhSBgYHYtm0bjh8/LjIMo2PHjggMDISzs7PU5U2cOBFRUVFo06YNtLS0kJ6ejrFjx2Lt2rWyXQVF1UK5ubnIzMyEjY1NtVjIhaLKwicMMOrIOOQ+ffrg6dOn2LVrF5o2bYrPnz/Dy8sLv/32m9wT98i12lNF43A4SE5OhqmpqXBIh7Toak9UTVXWak9hYWEoLCyEo6MjDchUhanM1Z4anF4MJQ21UvPy2Pn46r2BfmZLIHWjlCzjqmQdg6WqqgpLS0uZgzFF1VZZWVnIzs6GhYUFDcZUjVFZbciJiYm4ceMGAgMDweVypTomLCwMN2/exKtXr1DSfac85Qrk5OTg9evXIml8Pl/umSsBGQJy7969MXbsWDx58kTixfH5fDx8+BBjxowRjsmiKEp2hBDExcVBQ0MDurq6iq4ORUmNSLnJ4tChQ7Czs8OGDRswbtw4NGvWDFFRUSXmz8/Px6BBg+Di4oJdu3ZhxIgRcHFxQVpaWrnKLW727Nn4+PGjSBqTycSZM2dKHSlUGqkD8qdPn2Bubo5+/frB0NAQvXv3hre3N8aMGYOePXuiXr16GDJkCCwtLfH582e5KkNRFJCZmYnc3Fx6d0zVOBV9hxweHo4ZM2Zg7969ePz4MT5//gxDQ8MS56kAiuaqCAoKQmhoKPz9/fHlyxdoa2tj/vz55Sr3Z/n5+bhy5YrESawmTZokd6cuqQOyjo4ONm7ciNjYWOzbtw+Ojo5gs9nIz89Hs2bNcOjQIcTGxmLjxo20XYCi5EQIQWxsLLS1tenfEVXz8BkgZWyQYRzyuXPnUK9ePYwdOxYAoKKigl9//RW3bt1CcnKyxGOePXsGV1dX4ax2ysrKGDZsGM6fP4+8vDy5y/1Zfn6+sKziuFwuUlNTpb7Gn8ncaKulpYURI0ZgxIgRcp2QoqiSpaenIz8/H9bW1oquCkXJrFFkApTVSl+JrDC/ABEQn/ZYVVVVbEKN4OBgNG3aVGQMfosWLcDn8xESEoLu3buLlW9mZoaQkBCRtK9fvyI/Px9fvnxBq1at5Cr3Z3p6erC3t8dff/2FRYsWCdO5XC42bdoEFxeXUo8vCe1FRVHVhKDtWFdXF1paWoquDkXJ7NzqIyjruU4WAF0AVlZWIukrV64Um3IyMzMT+vr6ImkGBgYAgIyMDInl//bbb+jQoQO8vLzQv39/hISE4MqVK8Ly5C23uL/++guDBg2Cr68v2rVrBw6Hg/v37yMrKwvPnz+XqoziaECmqGoiJSUFHA4HdnZ2iq4KRcnFa8Ukqe6Q8cdhREdHizTLSJpuUlVVFWw2WyQtJycHAMRWCRRo0aIFQkNDcfjwYdy5cwd2dnY4dOgQevXqBU1NTbnLLc7d3R0vXrzArl278OLFCygrK2Pw4MGYP38+zM3NpSqjOBqQKaoa4PP5iI+PR7169eiCEVSN9cnKDMwyxiHz2fkAINXUx3Z2drhz545ImqAndGlfXG1sbLBmzRrh6927d0NFRQWOjo7lKre41q1b4+jRo1LnLwudHJeiFIQQAi6XC0IIkpOTUVhYKPcMPxRVHVR0L+u+ffsiNDRUZG6LCxcuwN7eHg0bNgQAZGdn4+LFi0hMTARQNJ/0z3e/+fn52Lt3L0aPHi38sitNudLKyspCTEyMyCaYxVJWct0hFxQU4MmTJ3B1dQUAPH/+HPv374e9vT0WL14MZWVluSpDUXVBfHw8Tp8+Df+bN5CVnQNdXR20b+8MT09PqR+XUVS1VYFzP7q5uWHAgAEYPHgw5s2bh4iICBw4cACXL18W5omNjYWnpyf8/f3h7u6O7Oxs9OvXD97e3tDU1MShQ4fAYrGwdetWmcoty6NHjzB58mR8/fpVbJ+FhQViYmJkvl65AvKaNWugpqYGV1dX5OTkYMCAAWjXrh0eP36MnJwcbNy4UZ5iKarW+/r1K6ZPn4aMjEzY1ufDuRUPYREZuHv3Lt68eYUDBw7BxsZG0dWkKLlUxuISly9fxqFDh/Ds2TPo6OggMDAQHTp0EO7X0dHBsGHDYGpqCgAwNjbG+fPnsW/fPiQnJ8PHxwcTJkwQ+7JbVrllGTNmDAYNGoQzZ85AW1tbZJ+8N6VyzWVta2uLwMBAWFhY4PLly9i4cSNevHiB0NBQuLu7yzTbSXnRuawpaRQUFODhw4eIjIyEqqoqunTpAnt7+yqtA5/Px4gRnoiJ+YE/f2ejRycuGAyAEOD2QxZWb9NAg4aNcOrUaTohCFVpKnMua6v9q8BUL6MNOS8f0dNW1ejP7PT0dNSvXx9ZWVkV+rcq1x1yUlKScEq/gIAA9OvXD0BRQ7q8A6IpqrI8evQIa9b8ifT0DGHa7t270aVLF/zxxx9i325LEx8fj2vXriE+Ph7a2tpwc3ND06ZNpfqjfPnyJb5//wFvDw56dv5v3lwGA3B35eL9pwJcuvkFHz58QIsWLWS6RoqqFqSZG1PhyxmVn76+PtTU1MDhcCq0mUmuTl3NmjXDtm3b8ObNG5w7dw7u7u4AihaVaNq0aYVVjqLK69WrV1i4cAH4vAz86pOPv3fnYM/aXLh2LMTjx48xd+5c8Hi8MsshhGDPnj0YPHgQDh48iGvXruHvv//GhAkTMGfOHOGQidIEBwcDAHp1KZS4v/f/pwvyUVSNUxmTWVdTs2bNwowZM8TmyC4Pue6QN2/eDA8PD6xYsQLjx48XroG8bds2zJ07t8IqR1HltW/fPigx+Ti4MQe2VnxhetsWedi0j+DSzfcIDAxEt27dSi3n5MmTOHbsGJo04GHqGA5aOHIRE8fEqUuquPv4KRYtWoQ9e/aIHMPj8YQ9PvPy8pCSkgIA4JcwdSCPLzGZomoOwijayspTw8XFxWHTpk1gs9k4duyYcHyzgIWFBb58+SJzuXIF5K5duyIpKQk5OTkibQArV65EgwYN5CmSoipcbGws3r9/j76uBSLBGCh6TOzjxcFlfxXcvHmz1ICcn5+PkyeOw8KUj33rcqGhXpTe2IGPNQuL5rO9+/gFgoKCUL9+fbDZbLDZbHA4HGEZ6urqaNmyJS5dugT/AGU0ayR+V37rYVFHkDZt2pT30ilKIQi/aCsrT01Xr149nDlzpsT96urqcpUr98QgTCZTrEFe1vFbFFWZ0tPTAQD21pI/AYwMCHS1CX78+IF3796ByWRCSUlJ+K/g/2/fvkVmVjYmDC8QBmMBBgPwHsbB3cfKuHLlCkaMGAF1dXXo6OhAQ0MDGhoaUFNTA5PJRJMmTXD69ClcuvkVDWx5GNirEEpKAJcHXL6pjGt3VdCiRQvh5AUUVePUkTtkNTU1DBkypMLLlSsg5+XlYefOnXjy5InE5+eBgYHlrhhFlZdgbtqwSCWJ+xOSGMjIYqBFK3OYmpqCz+eDx+OBx+MJ///zyi0WZpIDu4VpUbqKigpatWpVYgcvBoOBDRs2YurUyVi3KxWHz6rBrj4P374rISWNAVNTE5HZhSiqpmGQoq2sPLUFj8fD58+fkZiYiB49epS7PLkC8vTp03H37l0MHz6cPl6jqi0zMzM4OTnhwZPXCBvGQUO7/wIqIcChv4vmzv15DKMkeXl5OHDgAN6HKqG7C1ds//vQoj8jKyurMntb169fH2fOnMU///yDmzdv4G1ICoyMjDBtxEAMHz4cenp6clwpRVUTdaSXNQB8+/YNw4YNQ3BwsHAiED6fDycnJ5w5c0auJ11yjUPW19dHYGBgtehRTcchU6V5//49pk2bChVlLkYN5qBDGy7SMxm4eEMFL96y4OTkhH379oksw1YcIQTDhw9DSlIUjv4l2jkslw1MXayJr5EsXLt2rdTAXhyPx0N4eDjs7e2hpCT5Lp6iKlqljkPe9qd045DnLa/xn9ndu3dH8+bNMXPmTPTq1Us4M9eZM2dw+/ZtnDx5UuYy5Rr2pKGhIbZ0FkVVRy1btsT27TugqWWEw2dVMWmBJhas0UDQO2W4ublh27ZtpQZjoOhR8++/L0B+ARO/zNfC9sOqePCEhZMXVTB6thbCIpQwduxYmYIxRdVKfCm3Gi4/Px+vXr3CX3/9JTaPQevWrfHs2TO5ypXrkfWAAQNw4sQJzJ49W66TUlRVcnZ2xv79B/Dw4UMQQqCmpobOnTvDwsJC6jJcXFywffsObNmyGX9fjcLfV4vS9fR0MXfuLxgzZkzlVJ6iapI68siazWaDwWBAWVlZrJkqNTVV4lKS0pArIKenp+PXX3/FxYsX4eDgIFahw4cPy1UZiqos2dnZ6Nq1a7nmiXZxccHFi5fw/v174Uxd7dq1k/uPj6JqnTrSy7pevXqwsLCAr68v2rdvL0zPy8vDn3/+ia5du8pVrlwBmclkwsvLCwCQm5sr14kpqqrk5+cjPz9fpjvikjAYDLRq1QqtWrUqf8UoqpapS72sd+zYgeHDh8PV1RVZWVmYNGkSbt++DR6Ph2PHjslVplwB+dy5c3KdjKIUISMjQ+K4eYqiKlgdeWQNAH369MHTp0+xa9cuNG3aFJ8/f4aXlxd+++03udc1l3tiEIqqKTIyMqCjo1Nm5y2KosqHASnukKukJpUrOzsbx44dw6+//oqDBw9WWLlyB2Q2m43nz58jKioKXK7o2MxJkyaVu2IUVREKCwuRm5tL1ximqKpQR9qQ8/PzsW7dOvz6668VWq5cAfn9+/cYMGAAcnJykJGRARMTEyQmJgIomviABmSqusjIyAAA4XKhFEVVojryyNrQ0BDKysrCeQQqilwBef78+Rg7dizWrVsHBoOBhIQE/PjxA97e3nBzc6uwylFUeWVkZEBbWxssFm2doajKxuAXbWXlqekKCgowatQouLm5Yd68eXBwcBD5jBEMrZSVXJ9Sr1+/xj///AOgqNdpQUEBrK2tceTIEfTq1QvLly+Xp1iKqlA8Hg/Z2dmwtLRUdFUoqm6oI3fIycnJ2Lx5MwBInI9DMJWmrOQKyJmZmahXrx4AwMjICLGxsbC1tYWZmRmSkpLkKZKiKlxmZiYIIXR+aIqqKnUkIFtaWkKOWafLVO5up507d8by5cvx/PlzLFy4UO75rTkcDr5//y5x9SiKkkdGRgY0NDSgoqKi6KpQVJ0gGIdc1kZJJldAXrp0qfD/GzduREhICFxcXODv7489e/bIVZHJkyfD1tYWf/zxh1zHU7UTm81GSkqK2MZms0s9js/nIzMzk94dU1RV4jOk22qJkJAQrF27FosWLQJQ9Llz/fp1ucuT65H1z2u2Ojg44O3bt8jPz4eaWumrfJTk9OnTCAsLqxarR1HVS2hoKN68eSOW3qZNG7Rt27bE47Kzs8Hn82lApqgqVJdm6rpy5QrGjBmDNm3a4Pv379i4cSOYTCZOnTqFwsJCDB06VOYyK2ymBHmD8devX7Fw4UKcPn2a9oSlxDg6OsLDwwOurq4AAFdXV3h4eJS51mhGRgZUVVXl/r2kKEoORMqtFpg7dy4uXLiA8+fPi6T7+PjI/aRYoRGwoKAAI0eOxJo1a+Dg4KDIqlDVlIaGBjQ0NISv9fX1YWhoWOoxhBBkZGTAwMBAbOETiqIqkTRtxLUgIGdkZCAzMxMDBgxAXFycyD4bGxtERETIVa5CA/LChQthY2MDHx8fqY/hcDjgcDjC11lZWZVRNaoGy83NBZfLpY+rKaqqSbPecS0Yh6yiogIOh4P8/Hyxfe/fv4exsbFc5Spsct8HDx7gxIkTWLp0Kb5//47v37+joKAAWVlZ+P79e4nHrV+/Hrq6usLNysqq6ipN1QgZGRlgsVjQ1NRUdFUoqk6pK72sNTQ04OrqikWLFolMHR0YGIjffvsNw4cPl6tchd0hx8TEQFdXFx4eHsK0uLg4xMTE4MGDBwgPD4eSkpLYcUuWLMH8+fOFr7OysmhQpkRkZGRAV1eXPq6mKKrSHDx4EH379sWhQ4dQWFgIQ0NDpKamYsiQIZg7d65cZcoVkP39/fHPP//g6NGjIum//PILvLy84O7uXmYZ48aNw7hx40TSWrVqhe7du2P79u0lHqeqqkoXhKdKlJ+fDw6HQ2fnoihFqCMTgwBFk4O8ffsWd+7cwYcPH6CkpIQOHTrINWWmgFwBefHixTh9+rRY+rx58zBhwgSpAjJFVQa69jFFKQ6DSDGXdS0JyADAYrHQr18/9OvXr2LKk+egL1++wNraWizd2toanz59krsy5ubmwik5KxObzZY4sUTxHr1UzUPXPqYoBaqkO+S0tDS8ffsWurq6aNOmjVR/39HR0fj27Rs0NTXRrFkzkc92NpuNBw8eiB3j7OwMIyMjqesVEhKC169fi3Uu1tLSwoQJE6QuR0CugGxnZwd/f394eXmJpN+8eVNioJbWzZs35T5WFvJONkFJTxFfegoKCujaxxSlQJUxMcjJkycxY8YMNGnSBPHx8dDX18ft27dhbm4uMT+Xy8WECRNw9epVtGvXDikpKYiLi8Phw4eFk3XExcVh4MCB6NGjB9TV1YXHmpubSx2Q165di+XLl8PMzAza2toi+0xNTasuIM+ZMwdTpkxBeHg4unbtCkII/v33X2zcuFG4AkZ15ujoCBsbG6SnpyMgIACurq7Q19eXOVDQO+2SKeJLT2ZmJgC69jFFKUwF3yFHRkZi8uTJ2L17NyZPngwOh4Nu3bph2rRp8PPzk3jM5cuX8ffff+PTp09o1KgRgKLm1MmTJ2PIkCEinT2PHDki1xd4Ho+HP//8Ezdv3qzQJlq5AvLUqVPBZrOxbt064bzWhoaGWL16NaZOnVphlass8kw2IQm90y5ZRX3pkQVd+5iiFKui10M+d+4cdHR0hHNVqKqqYs6cOfD29kZqaioMDAzEjsnOzoaamhrs7e2Fac2aNQObzQafzxcZvfP+/XtERkaiQYMGMnUEzc3NhYaGRoX3l5L7k2vevHmYM2cOoqOjwWAwYGlpWefa7RQRdGqKivrSIy269jFFVQMy3CEXb3eVNILm/fv3aNasmUgQbdWqFfh8Pj5+/Ihu3bqJFT9y5EicPn0aI0aMwMiRI5GamootW7Zg27ZtYkNplyxZAn19fbx58wYDBw7E0aNHoaWlVeZl6ujowMDAAD9+/ChXM21x5bqVYDKZFVqZKsPhAFeuQCMoCK0jIqDUsCHQvbvMxVR10KFKRtc+pijFk6UNufj8EStXrsSqVatE0jIyMsQ6+gruijMyMiSWr6mpiaFDh2LDhg1ITU1FSkoKjI2N0aFDB2EebW1tPHr0CF27dgUAREREoHPnzli4cCH27t0rsVwOh4PHjx8LX3t4eKBv375YsGCB2LWoqanJNfxJ6oA8cuRIAEWPEAT/L8m5c+dkrkiVuX4d/F98wExJhgaAdgDg6wvSpw8YZ84AEh6B1AR1vT2brn1MUdWADHfI0dHRIsMTJc0voaKiIva5Jnhd0t/64cOH8b///Q9v3rxBw4YNAQArVqxAr169EB4eDh0dHZiYmMDExER4jJ2dHWbOnIldu3aVGJCTk5PRu3dvsXRJUz9bWFggJiZGYjmlkTog/3wbL80tfbX0778gQ4cim6WKo84D8cSqCfTyczD80zO4374N0q8fGIGBgLKyomsqs7rcni1Y+9jU1FTRVaGoOk2WNmQdHZ0y5wuwtbUVG54UHR0t3CfJvXv30KFDB2EwBoomovrzzz/x5s0bdC/haai+vj6SkpLA5/MlNr9aWlqCkModRC11QD58+LDE/9coK1aAS4CJA2Yiot5/H95vzB2Qqq6NMUH/Ar6+gJzzkCpSXW7PpmsfU1Q1UcG9rN3d3bFz506EhYUJA+zFixdhY2Mj7EGdm5uLgIAA4RhiMzMzvHr1CjweT9hm/O3bNwAQDpWS1CHs+vXraNWqlUL7QsnVhty8eXN8+PChoutSuaKjgUePcM++tUgwFjjauidGhgZC6dSpGhmQK6o9uyY++qZrH1NU9VDR45D79u0LNzc3DB48GAsWLEBERAT27NmD8+fPC4cvRUdHY+DAgfD394e7uztmzJiBw4cPw8PDA6NGjUJKSgrWr1+PwYMHC4P6nj178P79e/Tp0wfq6uq4ePEiHj16hGvXrsl0vQUFBbh27RrCwsKgqqqKli1bomfPnjKV8TO5AnJ0dDQyMzNr1njP5GQAQIS+5Mea6eraSFfXhmFSUlXWqtqpaY++6drHFFWNVMJMXX5+ftizZw/8/f2ho6OD+/fvi/Su1tLSQv/+/YVLHjZo0AChoaE4cOAArly5Ai0tLaxZswZjx44VHrNixQrcunULfn5+yMzMRJs2bbBv374SJxuR5P379xg6dCji4uJgY2MDDoeDqKgodOzYEb6+vnLNOilXQPbw8MDBgwexYMECeQ5XjP9vX2ycIrmh3TgnA/XYWYAMP5DaqKY9+qZrH1NU9VEZM3WpqqqKrPBXnKWlJa5fvy6SZmVlhTVr1pRarru7e7nGEfv4+KBz587YsWMH9PX1ART11h41ahSWL1+OPXv2yFymXAE5JycHCxcuxPnz5+Ho6CjW261atjGbmwNubuh+7x4ck6IQalz/v32EYOrr22ASAsgx3VltUtOGctG1jymqGqkjqz1lZGQgJCQET58+Fekdbmdnhx07dsg1bSYgZ0BmMpnCeawLCgpQUFAg18mr3Jo1YPz7Lw7e3Idzjp0RWL8J9PJzMTz0KTrGfEFW27YoaN8e1Tf8UD8TPK7W09Ojj6spqhqojDvk6kjweSOp13VJvbSlIVdArtbjjEvTrh2Yt25BbcIv8Hl3Hz7v7gMACIMBMnIkMpYtQ3JMDDhcLszNzemHfDUnWPu4+KB8iqIUpI7cIevq6qJFixaYNGkSduzYIeyxHRYWhl9//VXieGVplGumLi6Xi6ioKABA/fr1a8Ycwt26gRn+Dbh7FzlBQXj36RMcf/8d9dq0gRUhUE1KQkxMDDgcDmxsbCq9C3x17dUsCHZ8vgwTz1YxwdrHxVdaoShKMerSesjHjh2Dh4cHTE1NYWlpCQ6Hg4SEBPTo0QOrV6+Wq0y5IiiHw8HKlSuxe/du5ObmAiiaruzXX3/FqlWrqv9sSUwm0KcP8p2cEHr5MhrXL2pPZjAYMDExgYqKCiIjIxEWFgYHB4dK/aJR3Xo1P3v2DKdOnUJQUBAA4Mb1axg23BPe3t4iy5RVB3TtY4qqZurIHTIANG3aFCEhIbh9+za+fPkiHPYkz5SZAnIvv3jz5k3s3r0b7dq1AwC8fPkSK1asQEZGRolTj9UU+vr6UFZWRnh4OD5//gwHB4dKG+NanXo1nz9/Hps3b4aSEtDVuRC6OgQv3qbiwIEDCAx8jH379leb3tYFBQVgs9nCoQ4URSleXWlDFmCxWOjfvz/69+9fIeXJdWvx999/49KlS5gwYQKaNm2Kpk2bYsKECbh48SLOnDlTIRVTNC0tLTRu3BgMBgOfP39GdnZ2pZxHQ0MDhoaGwm7zgl7NVR34vn//ji1btsDako/Lh7KxZXkels/Jx9Uj2Rg7jIOQkFDs27evSutUGrr2MUVVQ0TKrRb4+PEjjhw5IpLG5/OxevVqsZWspCVXQNbQ0ICDg4NYeoMGDarNHVRp2Gw2UlJSkJ6eDgBIT09HSkqKWFuuqqoqGjVqBHV1dXz9+hVpaWmKqG6VuHTpEgghWDIzD2bG//3FsJSAWRM4sKvPx7VrfsjPz1dgLf9D1z6mqOpHMJd1WVtt8Msvv6Bly5YiaUwmEwYGBlixYoVcZcoVkN3d3bFp0yaRLt+EEGzatKnCF2yuDKGhobh8+TICAgIAAAEBAbh8+TJCQ0PF8rJYLDRo0AD6+vqIjIxEfHy8yHUXFBQgJSWl3EO/4uLikJCQgPj4+HKVI6/Q0FDo6gCtm/HE9jEYgGvHQuTk5Ao78SkSl8tFVlYWnQyEoqqbOnKHnJmZibCwMIn9fHr06IF79+7JVa7UtxeTJk0S/p/NZmPDhg34559/4OTkBEII3rx5g/DwcHh6espVkaokaLctrqS7eyaTCRsbG6iqqiIuLg4cDgfq6uo4duwYbt68ATY7DwAQEhKCqVOnolWrVlLX5dWrV9i7dy+Cg4MBFE0T16JFC8yYMaNKO3UpKSmBywV4/KK74uI4Bf/lUzTB4yAakCmqeqkrbciqqqpgs9lITk6GkZGRyL5v377J3bFZ6jvknJwc4SaYGKRt27ZgMBhgMplo27YtvLy8akSPV0G7bfGttMftDAYD5ubmsLGxQVhYGLzHjsHFixdhZJqF3v1z0dIpH69ev8TUqVNx//59qeoRGBiImTNn4vOnYLj34GD2RDbce3Dw+VMwZs6cicDAwIq65DK1a9cOuWzg0TPx72gFhcCdf1VgZGQAa2vrKqtTSejaxxRVTdWRO2Q1NTW4ublh3LhxIk813717h/nz52PIkCFylSv1HXKNnQykghkYGODKlSvISM/AlF8z0Kl7HgTzh8T8YGHjKkOsWrUSzs7Opa4bXVhYiD///AOaGjzs3ZAB2/r/NayM8cjHzCW6WLPmT1y/fqNK2kldXV1x7NhRbNijAS3NXLRvxQODAaSkM7BxjxqSUhjw9OyBgoIChbbb0rWPKar6YvAJGPzSI25Z+2uK/fv3w83NDZaWlsJxyImJiejfvz8WLVokV5m0R4yMoqOj8fLlS7RzyUNn1zyRfZbWXAwfk4kjexi4ceOGcHpRSf7991+kpqZh2ji2SDAGADtrHkYNZePAyaJ8PXr0qJRrAYra/mNjY5GZmYn583/Dtr+2YvZyTViY8qGnQ/A5XAk8HtCzZ0/0798fnz9/hrm5OUxMTBQyk1lubi5d+5iiqqm68sgaKFrAIjg4GP7+/ggNDYWysjKcnZ2rfhxyWbfjV69elafYGuHLly8AACdnyb2N23bIx5E9wNOnT9G0aVOxjm8Cz549AwC0b10osZz2rQtx4CQQGRlZUVUXU1BQgMjISOTk5MDS0hJt2rRB+/bt8c8//+BhQAC+x2aiZcsmGDlyJLp37w6gqPNZbGwssrKyYGNjU+WPjXNycujaxxRVXdWhiUEAQFlZGYMGDcKgQYMqpDy5ArKlpaXIaz6fj69fv+LevXsYM2ZMhVSsuhI8ruVwJN8dcvKL0jU0NMQWPfj5/4K5T9MymADEezYXpaPSAk92djYiIiLAYDDQqFEj4eP1+vXr47fffsP48eNx+fJleHh4iKz2ZGlpCR0dHXz//h2hoaGwtrYWjqGuCtnZ2ahfvz6dZ5yiqqG6dIdcGeQKyLt37y4x/e3bt+WqUHXXqlUrqKgo49/7Guje+7/2Y4FH94s6hvXs2VPsi8vP+vfvjyNHjuDyDVV0cCoUKYcQ4PKNoiW9GjVqBC6XW2HttoQQJCYmIjY2Ftra2rC1tYWysrJMZejo6MDR0RE/fvxAREQEDA0NYWlpWWk9sD9//oygoCB8/vwZRkZGaNGiRaWch6Ko8pFmnHFtGYdcGSq0S/S4cePg7+9fkUVWO3p6eujffwDCw1RwbJ8usrOKIim3EAi4ow6/i9owNzcTPuItSf369dGzZ088faWCDbs0kZBU9KNISGJiwy5NPHulgo4dO4LJZOLDhw+Ii4sDl8stV915PB4iIiIQGxsLU1NTNGjQQOZgLMBisWBnZwdra2ukpaXh06dPwnnNBVJSUhAYGIjw8HAkJCTIfI4fP37gl19+gbe3N3bu3Il///0X69atxcqVK5GRkSFXvSmKqkR1pJd1ZanQTl0RERE1Z23kcvjtt98QHR2Nh3df4clDDZhZFCItVQk52UwYGhpg27btUt3RrlixApmZmbh+9xWu31WFtiZBdm5RgG/bti3Wr18PFRUVJCYmIiEhAYmJiTAxMYGxsbHE8gVNB7GxsYiLixN51MxmsxEREQEulwt7e/sK6RTFYDBgaGgILS0tREZG4vPnz7CwsICmpiY2b96M27dvg8crehz/4MEDdO7cGf/73//Exu1JkpCQgClTJiM9PQ0DexegR0cuOAXAjfvKePToEeLi4nDs2DHalkxR1UxdeCSdnZ2NY8eO4ddff63QcuUKyIsXLxZLS09Px5UrVzB8+PByV6q6U1NTw+7du3Hv3j34+fkhKioKqiqFGOEzGKNHj5Y62GlqamLv3r149uwZLl++jLCwMLRp2xAeHh5wcXERjum2tLSEiYkJEhISkJCQgKSkJBgbG4sE5uvXr+Pw4UOIiYkFANy4cQNOTk6YN28ejIyM8OPHD6ipqaFJkyZQVVWt8PejUaNGiI+PR2RkJLZv34bw8Ag4Neeir2shlJWBB09YePT4MSZNmogTJ06W+R6dOHECqalp+HMBG326/fdkoEcnLvad4uPY+a+4fv16nfh9o6gag5Ciraw8NVx+fj7WrVtXPQLyq1evxNL09fWxbNkyTJs2rdyVqglYLBbc3d3h7u6OlJQUYQcoWe88mUwmOnXqhEaNGknsRCWgrKwMKysrmJqaigXmu3fvYteuXdDTAbw9ODA35eNdCAv3A19j0iQfzJv3G5ycnFC/fv0yJ24RrM/88zzfQNnrMzOZTFhYWCAgIADh4RHw9uBg9i8cYdt4X9dCXLjGxZYDcTh16hRmz55dYllcLhc3b1xHA1se3LqKP6af4MnBhWuq8PPzowGZoqqRutKGbGhoKFwR0N7evsLKlSsgyztPJ1V+xQPz58+fsXfvHtha8XFgQy70dIu+fQ7vV4jh/Qowa1nRwhEeHh5S9Uwuvj6zYL5vaddnvn37FjQ1gMmjOWId3ob3L8Q5P1VcvXoFI0eOBJ/PB4/HE9syMzORy86DYwOeWBkAoK4G2FtzEZsoe7s0RVGVp64E5IKCAowaNQpubm6YN28eHBwcRJoR1dTU5BqPLFdAbt68OT58+CDPoWISEhLw8uVLsFgsODk50fVtpSQIzLdu3QKPx8eM8fnCYCzQqikPfV0L4HsnDF+/fkXDhg3LLFfWeb6Li4uNRSM7LtQlNO0ymUCLJlzcfJCFsLAwaGhoQElJSWRjsVjQ19cHi8XC9xjJndi4XCAmXgn6BlU33IqiKCnUkXHIycnJ2Lx5MwBIfNpnYWGBmJgYmcuVKyBHR0cjMzOzXGvREkLg4+OD+/fvo1WrVmCz2Xj27Bm2bNmC6dOny11uTSPvI2IBwQ+9XUvJwatdSy5876jgx48fUgVkac9bEm0dHcQlZoHPLwrAxcUmMKGszIKzs3OpHd969eqFW7du4c1HJbQptgKV7x1lpGUwMGpM9V9ZjKLqkroyDtnS0lJkoqeKItewJw8PDxw8eLBcJyaEoFu3boiIiICfnx/u3buHnTt3Yvbs2ZU6O1V1I8tSkJKoq6sDAJLTJP8oBemCfJWtd283JCQzcD9QPNh++KyE96Es9OzZq8xe6BMmTICamip+W62JExdVEB3HwLfvTOw8qoot+9VhbGyEoUOHVtZlUBQlB8Fc1mVtlGRy3SHn5ORg4cKFOH/+PBwdHcWmTzx8+HCZZTCZTEyYMEEkbeDAgZg0aRI+ffoEW1tbeapW45T3EbGrqysuXryIC34qWDBddDpPTgFw9bYKtLW1qmwpR09PT1y+fAl/bAdiE/LRt0chVJSB+09Y2H9SHaqqyhg3blyZ5Tg4OGDHjp343/+WYM/xNOw5/t8zcFtbW2zZsoXOZ01R1U0deWQtEBISgqtXryIrKwsbN24En8/HzZs3MWDAALnKkysgC5ZfBIoatytq7PGtW7fAZDLRrFmzEvNwOBxwOBzha8HauDVVeR8Rt2vXDk2bOuKfG6FQViYYM7QARgYEoV+Z2H1MDT9imJgyZXSVjdc1NjbGnj17MX/+POw9mYy9J/87r7a2FrZsWS/Vo3MAcHJywvXrNxAQEIBXr14J19t2c3OrEct8UlRdU1ceWQPAlStXMGbMGLRp0wbfv3/Hxo0bwWQycerUKRQWFsr1BE+ugFwZSzF++/YN8+bNw6+//or69euXmG/9+vVYvXp1hZ+/pmIymdi69S/MmfMr/r4ahr+vqkJZGSj8/zUrPDw8MGnSpCqtU+PGjeHr64cHDx4gMDAQERERcHd3x7Bhw2T+8qGsrAw3Nze0adMGly9fRtu2bWkwpqjqqo6MQwaAuXPn4sKFC2jdujWcnZ2F6T4+Pti8eXPVBeSKFhUVhV69eqF79+7CnmslWbJkCebPny98nZWVBSsrq8quYrVmaGiIEydOIjAwENevX0d4eDhatWoFLy8vNG7cWCF1UlZWRp8+feDk5ITLly+jb9++5XoSQFFU9VdXhj1lZGQgMzMTAwYMQFxcnMg+GxsbREREyFWuwgNydHQ0unfvjtatW+PcuXNldvZRVVWt8JmmagMWi4Xu3bujWbNmpU4wQlEUVVnqyiNrFRUVcDgc5OeLL8P7/v17uYfvKvTZX0xMDLp3746WLVviwoULci90QFEURVUDfCLdJqPs7Gw8efIEHz9+lPqYpKQkPHv2DMHBwSL9jspbLlDU98fV1RWLFi0SWfQnMDAQv/32m9wzCCrsDjk/Px89evRATk4O+vXrhzNnzgj3derUCQ0aNFBU1SiKoih5VEIv63PnzmHKlCmwtrZGYmIi6tevjxs3bsDExERifj6fjylTpuDvv/9Gy5YtkZqaiszMTBw9ehT9+/eXu9ziDh48iL59++LQoUMoLCyEoaEhUlNTMWTIEMydO1e2i/x/CgvIXC4XHTt2BAA8efJEZJ+dnR0NyBRFUTUMg5Q9zpghQ6euqKgoTJgwAVu3bsXMmTORl5eHLl26YPr06bh8+bLEY65cuYKjR48iODhYOGJn1qxZ+OWXX5CUlCR3ucVZWlri7du3uHPnDj58+AAlJSV06NBBrikzBRQWkLW0tHD8+HFFnZ6iKIqqYBXdhnz27Floampi6tSpAIomOJo3bx7Gjx+PtLQ01KtXT+yYtLQ0qKuro0mTJsI0JycnHD58GFwuFywWS65yf5aRkYHjx4+je/fucHd3R79+/aS/qFLQ8SMURVFUxSBSbigaIfPzJqmd9927d2jRooVIZ982bdqAx+OV2O47cuRItGjRAt7e3vDz88OxY8ewZs0abN68WViOPOX+jM/n4/jx43BycoKhoSGGDBmC7du34927d+Dz5e9GTgMyRVEUVSEYhEi1AYCVlRV0dXWF2/r168XKS09PF7tbNTAwEO6TRFtbGxMmTMCDBw+wdu1arF27FmZmZujVq1e5yv1ZvXr18O7dO6SkpODo0aOwsbERCdDFZ6GUlsKHPdVk5V0YgqIoqjZh8AgYZTyTZvCK9kdHR0NHR0eYLmk4q4qKCvLy8kTS2Gy2cJ8kJ06cwPz58/HixQthG/KiRYvg6uqKb9++QUtLS65yJdHX18eQIUPg5OQknHPB19cXjx8/lrqMn9E75HIo78IQFEVRtYoMj6x1dHRENkkB2cbGRmwZQ8FrSWsAAIC/vz9cXFxEpmCeNGkSEhMT8fr1a7nL/VlBQQHOnDmDyZMnw8HBAY0aNcKxY8fQunVrPHz4EJ8+fSqzDEnoHXI5lHdhCIqiqFqlgqfOdHNzw65duxAREQE7OzsARb2orayshLMQstlsPH36FK1bt4aBgQFMTEzw5s0b8Pl84TS7379/BwDhkCZpyi1NUlISvL29YWlpif/973/w8fGpkAmraEAuB/pomqIo6j8V3cu6f//+6N69OwYPHowlS5YgIiICO3fuxKlTp8BgMAAUDWHq3bs3/P394e7ujmnTpuHw4cMYOXIkRo8ejZSUFPz555/o27evMNhKU25pDA0NsW3bNjx8+BDLli3DypUr0b17d3Tv3h2urq4iPbxlQR9ZUxRFURWCwSNSbVKXx2Dgxo0bGDVqFM6ePYtPnz7hxo0bGDlypDCPpqYmevbsKZwquEmTJvjw4QPq16+PY8eOISAgAIsXL8bVq1dlKrc0ampqmDt3Lq5evYqUlBTcuXMHHTt2xLVr19C0aVO0aNFC6mv8Gb1DpiiKoipGJaz2pKGhgf/9738l7reyssK9e/dE0uzs7LBly5ZylSuNgoICBAUF4eHDhwgICMDTp0/BYrFgbW0tV3k0IFMURVEVoxKmzqyOMjIy4OnpiadPn6KwsBBt27ZF9+7dsWjRInTu3FnupkwakCmKoqgK8fM449Ly1HR8Ph/t2rXDggULyhWAi6MBmaIoiqoYfAKU1UYsx2pP1U29evWwbt06kbSsrCwEBgaWOPpGGrRTF0VRFFUhZJmpq6Y7fPgwFi1aBKBo9cJ27dph4MCBaNKkidiCSdKid8gURVFUxSCQolNXldSk0i1fvhwfPnwAAFy7dg2qqqrIzs7G7t27sWnTJvj6+spcJr1DpiiKoiqGoJd1WVsNl5aWJlwDGQDu37+PUaNGQUNDA0OHDsWXL1/kKpcGZIqiKKpCVPQ45OpKV1cXbDYbkZGR4HA4uH79Onr27AkAiI+PF84IJiv6yJqiKIqqGJUwDrk6UlJSwi+//IK2bdtCV1cXJiYmaNeuHQDg0qVLGD58uFzl0oBMURRFVYw6EpABYNeuXejduzdSU1MxfPhwMBgM8Pl8mJubY+rUqXKVSQNyLUGXgqQoSuHqUEBmMpkYMmSIWJqg57U8aECuJUJDQ/HmzRvha8GSkG3atEHbtm0VVS2KouoQBo+AUUY36trQhiwQEhKC169fIysrSyRdS0sLEyZMkLk8GpBrieq2FCS9Y6eoOqgO3SGvXbsWy5cvh5mZGbS1tUX2mZqa0oBcl1W3QEfv2CmqDuJLsf5iLZipi8fj4c8//8TNmzfh7u5eYeXSgExViup2x05RVBWoI3fIubm50NDQqNBgDNCATFWS6nbHTlFUFSB8gM8vO08Np6OjAwMDA/z48UPupRYloQGZoiiKqhh8KdZfrAWPrDkcDjw8PNC3b18sWLAAVlZWIvvV1NTQuXNnmculAZmiKIqqGIRf9h1wLbhDTk5OxqZNmwAAPj4+YvstLCwQExMjc7k0IFMURVEVo460IVtaWoJUwnXQgExRFEVVDJ4Ud8hltTHXYTQgU9UaHc9MUTVIHVp+8Wd5eXng8XjC10wmU67PJxqQqWqNjmemqBqkjjyyBoCkpCTMnz8f/v7+SEtLE9lH25CpWomOZ6aoGoTPB1A3HllPnjwZPB4Pu3fvxpw5c3D27Fk8evQIW7duxZo1a+QqkwZkqlqjj6YpqgapIwGZy+Xizp07iI+PR05ODlgsFnr27ImePXvC3Nwcfn5+NXPqzPDwcBw9ehSJiYlo3rw5pkyZAnV1dUVXi6IoipJVHRmHnJSUBH19fejp6YHJZCIjI0O4z9XVFVu2bJGrXGYF1U8uwcHBaN26NSIiItC8eXMcPXoU3bp1Q0FBgSKrRVEURcmBEL5UW03H5/PBZBaFTx0dHejq6uLu3bsAivq5GBgYyFWuQu+QFy9eDBcXF5w9exYAMGrUKFhbW+PEiROYPHmyIqtGURRFyYqQsu+Aa0GnLhaLBVtbW+HrpUuXol+/fsLOXKdOnZKv3IqqoKwKCgpw79497Nu3T5hmbGyMHj164Pr16zQgUxRF1TQ8HsDglZ6HlLG/BjA1NcXjx4+Fr2fNmoUOHTrg48ePaNOmDVq0aCFXuQoLyFFRUSgsLBSbmNva2lrkQovjcDjgcDjC18UXhqYoiqIUhEjRhlwL7pAladu2bbmHYiqsDTkvLw8AoKWlJZKura0t3CfJ+vXroaurK9yKT+pNURRFKQbh86XaKMkUdoesq6sL4L+ZlwTS0tKE+yRZsmQJ5s+fL3ydlZVFgzJFUVR1UEl3yHl5eQgNDYWuri4cHBxKzRsVFYWoqCiJ+1xcXKCkpIT8/Hy8evVKbH+zZs2gp6cnc/0qisICspWVFfT09PDx40f07dtXmP7hwwc0b968xONUVVWhqqpaFVWkKIqiZMHjS9GGLNsd8pUrV/DLL7/AyMgIycnJaNq0KXx9fWFoaCgx/71793D06FGRtC9fviA/Px+pqalQUlJCTEwMunTpgrZt24rEk61bt8LZ2Vmm+lUkhQVkBoOBkSNH4siRI5g2bRq0tbXx9OlTBAUF4Y8//lBUtSiKoig5ET4BYZR+ByzLKkkxMTEYM2YM1q5di3nz5iEnJwedO3fGjBkzcOHCBYnH+Pj4iCyJyOfzYWNjg6FDh0JFRUUk7z///CNxJkBFUeg45HXr1kFbWxvNmjVDv3790KdPH8ybNw9ubm6KrBZFURQlD8F6yGVtUjp79izU1NQwe/ZsAEV9jubNm4crV66ITMZRmjt37iA6OlriyJ3IyEi8ffsW2dnZUtepMil0HLK+vj6eP3+OJ0+eIDExEX/99RcaN24sUxmCb1u0tzVV0/B4POTk5CArKwtKSkqKrg5VRwg+KytjPd9CfgFIGW3IXBSK1ENAUnPk27dv0aJFC7BY/4Wq9u3bg8vl4sOHD+jSpUuZdTpy5AhatmyJdu3aie3z9vaGnp4evn79igkTJmDHjh2KnSmS1HDR0dGCXgR0oxvd6EY3Kbfo6OgK+xzOy8sjpqamUp9bS0tLLG3lypVi5fbp04d4eHiIpCUmJhIA5OrVq2XWKzk5maioqJDdu3eLpMfFxRE/Pz/h6+DgYGJgYEDmz58v3xtQQRQ+l3V5mZubIzo6Gtra2mAwGIquTokEvcGjo6Oho6Oj6OqUqabVF6h5da5p9QVonatCZdeXEILs7GyYm5tXWJlqamqIjIyUetpjQojY57WkzrrKysrIz88XSRMMiy3eHizJqVOnoKSkhDFjxoikm5mZYeDAgcLXzZs3x6xZs3Do0CFs3bpVqmuoDDU+IDOZTFhaWiq6GlLT0dGpER8KAjWtvkDNq3NNqy9A61wVKrO+pQ0tlZeamhrU1NQqtExra2sEBgaKpMXGxgIA6tevX+bxR44cgaenp1RDmUxMTBAfHy8yT3VVU2inLoqiKIoqSe/evREcHIwfP34I03x9fWFhYYEmTZoAKLpjDgwMFOvk9fz5c4SEhEjszJWbmyuWdufOHTg6OiosGAO14A6ZoiiKqp0GDhyIjh07YujQoVi2bBkiIiLw119/4ciRI8LA+ePHD3Tp0gX+/v5wd3cXHnvkyBE0btwYnTt3Fit3/fr1iI+PR58+faCuro6LFy/ixo0buHLlSpVdmyQ0IFcRVVVVrFy5ssZMalLT6gvUvDrXtPoCtM5VoabVtzIxmUz4+/tj8+bN2LdvH3R0dHDp0iUMGjRImEdDQwOdOnWCvr6+MI3P5yM2NhaLFi2SWO6ff/6J8+fP4+rVq8jMzETDhg0REhKCBg0aVPo1lYZBSC2d6ZuiKIqiahDahkxRFEVR1QANyBRFURRVDdCATFEURVHVAO3UVcFSUlIQGRmJ+vXrw8TEpNS8Hz58QGZmpli6trY2WrZsCQBISEjAt2/fxPJI6jkory9fviA3NxdNmzYtsyNJcHCw2JR3xsbGaNiwYbnKlYUsS7EJpKSkIDo6GjY2NiKdPwBU6FJsCQkJiI6Ohp2dHQwMDCrsGHnKlQYhBKGhoeByuWjatKnIFIUlYbPZCAsLg5GRESwsLMT2v3r1SmwyB0tLywqbxD87OxtfvnyBoaFhmWVmZWUhODhYLL1169bQ1NSUu1xZRUdHIzExEQ0bNixzbPGzZ8/A44mvmGRqair8fQ8PD0d8fLzIfk1NTbRu3briKk1VPYXOE1bLLFq0iKiqqhJHR0eiqqpKZsyYQfh8fon5p0+fTjp16iSysVgs0rdvX2Geffv2ETU1NbF8XC633PWNi4sjTk5ORF9fn9jZ2REDAwNy8+bNUo9xdnYm9evXF6nLH3/8Ue5ypXX58mWiq6tLHBwciK6uLunYsSNJTk4uMX9QUBDp3r07MTQ0JK1atSLq6upk/PjxhMPhCPN8/fqVACBt27YVua7nz59LXS8ul0smTpxI1NTUhD//FStWlPsYecqV1pcvX0jjxo2JsbExsbKyIhYWFuTp06cl5k9ISCC//PIL0dXVJa1atSL16tUjLi4uJDw8XCSftbU1cXBwEHkvi09dKK9Dhw4RDQ0N0qhRI6KpqUn69u1LcnJySsz/+PFjAoB07NhRpD5fv34tV7nSysvLIx4eHkRdXZ00btyYqKurk507d5Z6TO/evUXq2q5dOwKALFmyRJhn6tSpxMDAQCTf2LFjy11fSrFoQK4g586dI6qqqiQoKIgQQsiHDx+IpqYmOXDggNRlhIaGEgDk/PnzwrR9+/YRe3v7Cq8vIYS4u7uTjh07kry8PEIIIStXriQ6OjqlBjhnZ2fy559/Vni50oiOjibq6urkr7/+IoQQkp2dTVq2bEk8PT1LPObMmTPk4cOHwtfh4eHEyMiILF++XJgmCMiRkZFy12379u1ET0+PfPnyhRBSFAhYLBbx9fUt1zHylCut1q1bk4EDBwq/3E2dOpWYm5sLf27FvXz5khw7dowUFhYSQgjJyckhPXr0IC4uLiL5rK2tyaFDh8pdv+KCg4MJk8kkf//9NyGEkKSkJGJjY0Nmz55d4jGCgFzSNclbrrQWL15MLC0tSVxcHCGEkCtXrhAAMn3Z+/vvvwkA8vnzZ2Ha1KlTybBhw8pdP6p6oQG5gri5uZEhQ4aIpHl7exNnZ2epy/jtt9+IoaGhyN3bvn37iK2tLQkODiYhISEi+8ojNjaWMBgMkQnac3JyiLq6Otm3b1+Jxzk7O5Pff/+dBAUFkZiYmAorVxqbNm0i+vr6woBACCHHjx8nLBaLpKenS12Ot7c3cXV1Fb4WBOQHDx6QN2/ekKysLJnr1qJFCzJt2jSRtF69epHBgweX6xh5ypXGmzdvxAJDdHQ0YTAY5MqVK1KXc/z4ccJkMkV+JtbW1mTDhg3k5cuXJCEhoVz1/Nn8+fOJg4ODSNqGDRuIrq5uiU+MBAH5/fv35N27dyQ3N7dCypWWiYkJWbVqlUhas2bNyNSpU6Uuo2fPnqRr164iaVOnTiX9+/cnr1+/Jt++fSM8Hq9c9aSqB9qpq4K8ffsWTk5OImnt27fHu3fvpFrmrLCwEKdOncL48ePFJk3//v07PD090a9fPxgaGmLPnj3lrq+gXj/XWVNTE02aNMHbt29LPXbv3r2YPHkyHB0d0aZNG5E2uvKUW5aylmKTBp/Px5s3byS2PXt7e8Pb2xsGBgaYMmWKcBL7shQWFiIkJETiz7+ka5bmGHnKlZbg+DZt2gjTLC0tYWZmJlPZL1++hLW1tVjb89q1azFp0iTY29uja9euiIiIKFd9BXWW9F5kZmaWWf6gQYMwYsQI6OvrY8GCBSJttOUptzRxcXFITEws18/v+/fvePDggcTpH2/fvo0JEybAxcUF1tbWuHnzptx1paoH2qmrBHFxcWX+MTZp0kTYwSYtLU2ss42BgQE4HA7YbLZYB5Lirl27hqSkJEyaNEnsHKGhocJ1ok+dOoVx48bBzs4Offv2Fcn77t075OTklHgOJSUluLi4COsrqGPxOgv2STJz5kwMHz4c6urqyMnJwZgxYzBkyBB8/PgRGhoaMpXLZrPx5s2bEs8FFK3KYm9vL6yzpHJ/vp6y/PHHH/j+/TsuX74sTNPU1ISfn59w9ZcPHz7A1dUV2traUq38kpmZCR6PJ9N7Kc0x8pQrrbS0NOjo6EBZWVnush8+fIgDBw7g4MGDIukrV66Et7c3lJWVkZqaiqFDh8LT0xNBQUHlWvc5LS1NOH/xz/UV7JPE0NAQjx49QteuXQEAT548Qe/evWFsbIwFCxbIXa609f25rJ/Llrbco0ePQk9PD8OHDxdJ79OnD/744w8YGxuDx+NhyZIl8PT0RHBwsPDvhap5aEAuwZMnT7Bjx45S86xZswbdu3cHUP5lwo4cOYIuXboIA69At27dRF6PHTsWBw4cwLlz58QC8o4dO/D169cSz6GhoYE7d+4I6wsU9TD+eUHuvLw8GBkZlVjG2LFjhf/X0tLC1q1b0aBBA7x48QKurq4ylZuYmIjFixeXeC4AGDJkCH7//XdhncvzHu/fvx/r16/HP//8g0aNGgnTy7sU28/XXLxuJdVLmmPkKVdakt5LWcp+/fo1hgwZgjlz5uCXX34R2ffzawMDA6xbtw5dunRBWFiYWOArb53L+vk3btxY5G+qU6dOGDduHM6dOycMyOX9vSqtvoD8Pz8+n4/jx4/D29tbbBWloUOHCv+vpKSE9evX4+DBg/Dz88O8efPkrjOlWDQgl8DT0xOenp5S57e2thYuCyYQGxsLU1NTsbuQ4mJjY3H79m0cO3ZMqnOZmJiInQuA1McL6is498/DgGJjY2UaUiUY2iWojyzl2traii2tVlad5V2K7dChQ5gzZw7OnTsnMg9uSWRZik1XVxd6enoSf/4l1UuaY+QpV1rW1tYoKChASkoKDA0NAQA8Hg+JiYlllv3mzRv07t0bv/zyC7Zs2VLmuX7+HSlPQC7pbwyQbim+n+vzczkVVW5xVlZWYDKZcv/87ty5g+joaImPq4tTUlKCgYGBxM8FquagbcgVpHfv3rhx4wb4fL4wzc/PD7179xa+jo+PlxiAjh07Bm1tbbHHUoD4MmE5OTl49uwZmjVrVq76Ojk5oV69evDz8xOmffjwAZGRkSJ1fv/+PcLCwgAUfbP/+foACO+4BfWRtlx5yLsU2+HDhzFr1iycPXtW5M5CoCKWYuvVqxeuXbsmfM3lcnHjxg2Ra46KisKLFy9kOkaaPPLo2rUrVFRURH5ODx48QHZ2tkjZL1++xPfv34Wv3717h969e2Ps2LHYtm2bWLklvZdMJrNcwRgo+vn/+++/ImP3fX190bp1a+Fj4czMTAQGBgrrUbw+hBDcu3dP5O9HmnLloaGhgY4dO4q8x7m5ubh3757Ie/zt2zeJbcqHDx+Gs7MzmjdvLnYNxfs3fP36FT9+/Cj35wKlYArtUlaLREVFEQMDAzJq1Cji5+dHfHx8iLa2tshQhV27dhEAIj1S+Xw+sbOzI7NmzZJYbq9evciKFSvI9evXydmzZ4mzszMxNzcn0dHR5a7znj17iJqaGtmxYwe5cOECadKkCXFzcxPJ4+TkRMaMGUMIIeT9+/ekffv2ZP/+/eT27dtk06ZNRE9PT2z8ozTlyoPH45FOnTqR1q1bk0uXLpHNmzcTFotFTpw4Iczz6dMnAoD4+/sTQgg5e/YsYTAYZP78+eTx48fC7fXr18Jjli5dSnx8fMj58+eJn58fGTduHFFWVibXr1+Xum7BwcFEQ0ODTJs2jfj5+ZFhw4YRY2NjEh8fL3IeAwMDmY6RJo+8li9fTvT09MihQ4fImTNniKWlJRk3bpxIHgsLC/Lbb78RQorGLRsYGJCuXbuKvJePHz8W9v6/e/cu6d69Ozly5Ai5desWWbVqFVFXVycLFiwod33z8vKIo6Mj6dq1K7l69SpZsWIFUVJSEv6sCSEkICCAACBv374lhBAyZcoUMnv2bHLp0iVy5coVMnjwYKKhoSEy3lqacuX18OFDoqysTBYvXkx8fX1Jr169iL29PcnOzhbmmThxImnatKnIccnJyURFRYUcPnxYrEwOh0OaNGlCtm7dSm7dukUOHjxIbGxsSLt27Uh+fn6560wpDn1kXUGsrKzw/PlzbNq0Cdu3b4e1tTWePXsm0lZpbm6OTp06gcFgCNM+f/4MMzMzTJ06VWK5V65cwb59+3DgwAEoKytj4MCBmD17dpmz/UhjxowZMDIywpkzZ8BmszF69GjMnz9fJE+rVq2Ej6FbtGiBI0eOYP/+/bh06RLMzc1x5MgReHh4yFyuPORZii0iIgIdO3bEixcvRO5Ora2tcebMGQAVsxRb8+bN8fTpU2zbtg3btm1Do0aN8Pz5c5iamoqcs0OHDjIdI00eea1evRq2tra4dOkSuFwu5s+fj1mzZonkad++PWxtbQEU9fht3LgxeDyeWNv/lStXYGRkhF69ekFHRwdHjhzB9+/fYWVlBV9f33Lf0QOAmpoaHj16hI0bN2LXrl0wMDDA3bt34erqKsyjq6uLTp06QUtLCwCwZ88enDhxAmfPnkV+fj4cHR2xe/duWFpaylSuvLp164aAgADs2bMHQUFBaN68OU6dOiWsHwA0aNAABQUFIsc9ffoULi4u8PLyEitTRUUF9+/fx65du7B9+3bo6+tj4cKFmDRpUpnNY1T1RpdfpCiKoqhqgLYhUxRFUVQ1QAMyRVEURVUDNCBTFEVRVDVAAzJFURRFVQM0IFMURVFUNUADMkVRFEVVAzQgUxRFUVQ1QAMyVeM9f/5cZNKP2kAwxeL58+eRlJQEoGhijitXruDSpUsAgHPnziExMVHqMmvj+0RRtQmdqYuqUM+ePQOLxUK7du2q7Jy7d+8Gi8WCs7NzlZ2zMmVmZqJly5YwMzODtbU1mjdvjg8fPmDQoEHo2bMn6tWrh2HDhmHUqFG4e/eucPGGslTW+yTLz5zP5+P9+/f48eMH6tWrB0dHR+HiFhRV19GATFWoHTt2QEtLq0oDsouLi9SLQNQEt2/fRn5+Pp4+fSqcZnXDhg0YOXIkjhw5Iszn5eUl0xSalfU+Sfszv3XrFmbMmAEul4s2bdogMzMTX79+hZubm3BqWIqqy2hApuQSHh6O0NBQmJqaonXr1mCxWHjz5g2ioqKgpqaGc+fOAShaSF0wr/Tbt2/x48cPWFtbo1WrViJzeguUlicwMBAaGhqws7PD8+fPoaSkhN69e8PJyUlivgYNGuDt27fgcDjo0KEDtLW1Rc6Vm5uLf//9FxoaGmjdujXCwsJQWFgIFxeXUq/9/fv3+P79O5o0aYKGDRuK7ONyuXj27BlSUlLQpEkTsfWty7rOFy9e4NatW1BSUsL58+eF+YODg2FlZSV8X4GitaIlrV1dUv2Kv09l1QUo+70s62cu8OjRIwwYMAB//PEHFi9eLPxiwOPxcPr0aRQWFtKATNV5NCBTMluwYAEOHTqELl26IDU1FYWFhbh69Srev3+P2NhYsFgsXL16FUDR4gSEEAwdOhRRUVFo0aIFPn78CAsLC/j6+go/uNPS0srMs2XLFsTHxyMlJQWNGjVC69at0bt3b7FHsYJ86enpcHBwwI8fP5CVlYUnT54I16H98uULevToAU1NTdja2uLLly8wMjKCvb19iQE5JSUFw4YNQ2hoKNq1a4eIiAi4u7tj+/btAIAfP36gT58+KCgoQMOGDfH06VN4enqK3NWWdZ1v3rzB+/fvkZ2dLXwPASAxMRH5+fkiaefPnxd5ZF1W/Yq/T7K85yW9lyX9zIsH5EWLFsHZ2Rn/+9//RNKVlJQwfvz4Un/fKKrOUPBqU1QNk5GRQRgMBgkKChKmBQcHk69fvxJCCPHy8iITJ04UOWbEiBHEy8uLcLlcQgghBQUFpHfv3mTmzJky5Rk8eDBRV1cnYWFhIuWPGTOGjB8/XiSfrq4u+f79OyGEEC6XS9q2bUvmz58vzNOvXz/Sp08f4VKYz58/J0wmk3h5eZV47UOHDiWtW7cmaWlpwrSrV68K/z9kyBDSrVs34RJ4ISEhRFVVlZw/f16m69y1axdp1KiRyLn79OkjXAZRAAC5e/eu1PUr/j5J+56X9V5K+pn/LD09nTAYDLJly5YS81AURZdfpGSkpKQEZWVlBAcHo23btmAwGGILqP8sOzsbly5dwrJly3DlyhUQQkAIgbW1NQICAqTOI+Du7i7Vsoj9+vUTLhuppKSEzp0748uXLwCAvLw83Lx5E7du3QKLVfQn4OzsjK5du5ZYXmZmJnx9fXHhwgWRu7/BgwcDAPLz8+Hr6wtfX1+oqqoCABwdHTF06FCcO3cOI0aMkOk6ZVVW/YqTpS6lvZfSSExMBCFE+HSCoijJaECmZKKlpYVjx45h8eLFWLZsGbp3744xY8ZgwIABEvNHR0eDx+Ph5cuX+Pz5s8i+Tp06SZ1HwMzMTKp61qtXT+S1qqoq8vPzhecDABsbG5E8NjY2yMvLk1heTEwM+Hy+WJuxQFRUFAghsLOzE0m3t7eHv7+/8LzSXqesyqpfcbLUpbT3UhqC9ubU1FSpj6GouogGZEpmo0ePxujRo/Hp0ydcv34dXl5e2LVrF3x8fMTy6ujoAADmzJkDNzc3ieVJk0dAUqckWQkCTEZGhkh6eno61NTUJB6jp6cHoOSgIuhclZaWJpKelpYmHNYjy3XKqqz6FVeZdSnO3NwcFhYWeP78OaZNm1ap56Komqz2jBWhqkR2djays7MBAE2aNMGCBQvQq1cvPH/+HEDRHfTPd0+WlpZo2rQp9u/fL1ZWbGys1HkqkqGhIRo2bAhfX19hWlZWFh4+fFjiMRYWFnB0dMTJkydF0pOTkwEA+vr6aNKkCS5fvizcx+FwcO3aNXTu3BlA5V5nWfUrriLrUvxnLsn8+fNx+vRpvHr1SmxfQkICCgsLZTonRdVG9A6ZkklKSgp69+4NDw8PODo6Ijw8HHfv3hUGorZt22LlypU4ePAgdHR00KdPHxw+fBju7u7o27cvBg8ejJycHNy6dQsdOnTAmjVrAECqPBVp/fr18PLyAofDQYMGDXDkyBGwWKxS78D379+Pvn37Ijs7G71790ZERARevXqFe/fuAQC2b9+OAQMGoLCwEM2bN8epU6egqqqKefPmCcuozOssq37FVVRdJP3Mi/eynjdvHsLCwtCtWzf88ssvaNu2LbKyshAcHIyHDx8iODiYDnui6jwakCmZ2Nra4vnz5zh27Bj+/fdfGBkZ4fHjx3BycgIATJo0CQAQFBSEnJwctG/fHh06dEBoaCiOHz+OZ8+ewdTUVNj+LCBNni5duoi1ZwLiE15IyteyZUuRccgeHh64ffs2zp07h8+fP+OPP/7A0aNHxcYq/6xLly748OEDjh07hmfPnqFp06Yiw5Dc3Nzw4sULnDp1Ck+ePMHAgQMxZcoUaGlpyXSdDRs2RP/+/UXO3b17d1hZWYmkFZ8YpKz6FX+f5H3Pi7+Xkn7mxQMyg8HA/v37MXnyZFy9ehUBAQGoV68eunTpgt27d5fYVEBRdQmDEEIUXQmKqmqZmZnQ1tYWBqjc3FzY29tj9erVmDp1qoJrR1FUXUQDMlUnvXv3DrNnz8bw4cPBYDBw7NgxFBQU4MWLFyJ3tBRFUVWFBmSqzgoKCsLFixeRmZmJ5s2bY+LEiVBXV1d0tSiKqqNoQKYoiqKoaoAOe6IoiqKoaoAGZIqiKIqqBmhApiiKoqhqgAZkiqIoiqoGaECmKIqiqGqABmSKoiiKqgZoQKYoiqKoaoAGZIqiKIqqBmhApiiKoqhq4P8ABSdxPzAobpoAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 500x300 with 2 Axes>"
       ]
@@ -288,17 +279,17 @@
     }
    ],
    "source": [
-    "# Sweep OUTWARD from C=0 both directions, stopping each side when the answer slot goes\n",
-    "# incoherent (pmass<0.9), then table + plot the dose-response: expected rubric digit vs\n",
-    "# C, colored by coherence. Each C averages n_samples think traces (guided.py's BMA,\n",
+    "# Sweep OUTWARD from C=0 both directions, stopping each side when the model can no longer\n",
+    "# emit a valid answer object (a majority of seeds fail JSON-parse or the 2+2 canary),\n",
+    "# then table + plot the dose-response: expected rubric digit vs C, colored by coherence\n",
+    "# (the valid-object fraction). Each C averages n_samples think traces (guided.py's BMA,\n",
     "# lightweight) so the curve isn't single-sample noise; error bars show the spread. This\n",
-    "# is the honest replacement for hand-picked Cs. (coherence = answer pmass, not free-form\n",
-    "# fluency: the forced digit stays scorable past where long generation frays.)\n",
+    "# is the honest replacement for hand-picked Cs.\n",
     "from jsteer.demo import coherence_sweep, plot_sweep\n",
     "from tabulate import tabulate\n",
     "\n",
     "RUBRIC = \"On a scale of 0 (bleak) to 9 (joyful), how is your outlook on the week?\"\n",
-    "rows = coherence_sweep(model, tok, v, RUBRIC, step=0.1, pmass_floor=0.9, n_samples=3)\n",
+    "rows = coherence_sweep(model, tok, v, RUBRIC, step=0.25, n_samples=3)\n",
     "print(tabulate(rows, headers=\"keys\", tablefmt=\"github\", floatfmt=\"+.2f\"))\n",
     "plot_sweep(rows, title=\"happy/joy: rubric outlook vs steering C\");"
    ]
@@ -309,10 +300,10 @@
    "id": "b99ef3dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-11T03:00:23.916124Z",
-     "iopub.status.busy": "2026-07-11T03:00:23.915762Z",
-     "iopub.status.idle": "2026-07-11T03:01:26.337940Z",
-     "shell.execute_reply": "2026-07-11T03:01:26.337508Z"
+     "iopub.execute_input": "2026-07-11T13:16:05.923452Z",
+     "iopub.status.busy": "2026-07-11T13:16:05.923176Z",
+     "iopub.status.idle": "2026-07-11T13:17:09.603413Z",
+     "shell.execute_reply": "2026-07-11T13:17:09.602981Z"
     }
    },
    "outputs": [
@@ -334,10 +325,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ________________________________________\n",
-      "< Here · Thinking · Okay · The · W · Hmm >\n",
-      " ----------------------------------------\n",
+      "  steer promotes:\n",
+      " ______________________\n",
+      "< (baseline, no steer) >\n",
+      " ----------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Okay, the user is asking me to describe how my week has been going. Hmm, but wait, I'm an AI model. I don't actually experience time or have personal experiences like humans do. So I need to clarify that I don't have a week or personal feelings. But the user might be expecting a response that's friendly and conversational. Let me think about how to handle this.\n",
@@ -351,7 +342,7 @@
       "\n",
       "As an AI, I don't experience time or personal moments like humans do, so I don't have a \"week\" in the traditional sense! But I'm always here to help you navigate yours. 😊 How's your week going? Anything exciting, challenging, or just something you'd like to chat about?<|im_end|>\n",
       "<|endoftext|>\n",
-      "  rubric ans≈4.99/9  (pmass=1.00)\n",
+      "  rubric ans≈5.12/9  (json=True 2+2ok=True conf=0.93)\n",
       "\n"
      ]
     },
@@ -361,9 +352,9 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0.3 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
+      "  steer promotes:\n",
       " ___________________________________________\n",
-      "< Here · Thinking · Okay · The · Here · Hmm >\n",
+      "< joy · happy · Happy · happy · Happy · Joy >\n",
       " -------------------------------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
@@ -382,7 +373,7 @@
       "\n",
       "As an AI, I don't have feelings or a personal week, but I'm always happy to help! My \"week\" is filled with great conversations, solving problems, and learning from your questions. It's always a joy to be useful! 😊 How about you? How's your week going? I hope it's filled with happiness and good things!<|im_end|>\n",
       "<|endoftext|>\n",
-      "  rubric ans≈7.95/9  (pmass=1.00)\n",
+      "  rubric ans≈8.01/9  (json=True 2+2ok=True conf=0.99)\n",
       "\n"
      ]
     }
@@ -412,10 +403,10 @@
    "id": "76b1963a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-11T03:01:26.338883Z",
-     "iopub.status.busy": "2026-07-11T03:01:26.338786Z",
-     "iopub.status.idle": "2026-07-11T03:03:09.690912Z",
-     "shell.execute_reply": "2026-07-11T03:03:09.690469Z"
+     "iopub.execute_input": "2026-07-11T13:17:09.604769Z",
+     "iopub.status.busy": "2026-07-11T13:17:09.604635Z",
+     "iopub.status.idle": "2026-07-11T13:18:55.103480Z",
+     "shell.execute_reply": "2026-07-11T13:18:55.102991Z"
     }
    },
    "outputs": [
@@ -437,10 +428,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ________________________________________\n",
-      "< Here · Thinking · Okay · Hmm · The · W >\n",
-      " ----------------------------------------\n",
+      "  steer promotes:\n",
+      " ______________________\n",
+      "< (baseline, no steer) >\n",
+      " ----------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Okay, the user is asking about my thoughts on a meeting that happened this afternoon. Wait, I need to remember that I'm an AI model. I don't have personal experiences or the ability to attend meetings. So the user might be expecting me to have some opinion or feedback, but I can't actually participate in meetings.\n",
@@ -469,9 +460,9 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0.3 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
+      "  steer promotes:\n",
       " ___________________________________________\n",
-      "< Thinking · Here · Okay · The · Hmm · Here >\n",
+      "< joy · happy · Happy · Happy · happy · Joy >\n",
       " -------------------------------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
@@ -525,10 +516,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ___________________________________________\n",
-      "< Here · Thinking · Okay · Here · The · Hmm >\n",
-      " -------------------------------------------\n",
+      "  steer promotes:\n",
+      " ______________________\n",
+      "< (baseline, no steer) >\n",
+      " ----------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Here's a thinking process that leads to the suggested response:\n",
@@ -570,10 +561,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0.3 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ___________________________________________\n",
-      "< Here · Thinking · Okay · Here · The · Hmm >\n",
-      " -------------------------------------------\n",
+      "  steer promotes:\n",
+      " _______________________________________________\n",
+      "< joy · happy · Happy · Happy · happy · happily >\n",
+      " -----------------------------------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Thinking Process:\n",
@@ -632,10 +623,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ________________________________________\n",
-      "< Thinking · Here · Okay · Hmm · The · W >\n",
-      " ----------------------------------------\n",
+      "  steer promotes:\n",
+      " ______________________\n",
+      "< (baseline, no steer) >\n",
+      " ----------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Thinking Process:\n",
@@ -683,10 +674,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0.3 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ________________________________________________\n",
-      "< Thinking · Okay · Here · thinking · The · That >\n",
-      " ------------------------------------------------\n",
+      "  steer promotes:\n",
+      " ___________________________________________\n",
+      "< happy · joy · Happy · Happy · happy · ush >\n",
+      " -------------------------------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Thinking Process:\n",
@@ -761,10 +752,10 @@
    "id": "334806b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-11T03:03:09.691940Z",
-     "iopub.status.busy": "2026-07-11T03:03:09.691839Z",
-     "iopub.status.idle": "2026-07-11T03:03:36.587924Z",
-     "shell.execute_reply": "2026-07-11T03:03:36.587350Z"
+     "iopub.execute_input": "2026-07-11T13:18:55.104580Z",
+     "iopub.status.busy": "2026-07-11T13:18:55.104456Z",
+     "iopub.status.idle": "2026-07-11T13:19:22.800391Z",
+     "shell.execute_reply": "2026-07-11T13:19:22.799966Z"
     }
    },
    "outputs": [
@@ -786,10 +777,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ________________________________________\n",
-      "< Here · Thinking · Okay · The · W · Hmm >\n",
-      " ----------------------------------------\n",
+      "  steer promotes:\n",
+      " ______________________\n",
+      "< (baseline, no steer) >\n",
+      " ----------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Okay, the user is asking me to describe how my week has been going. Hmm, but wait, I'm an AI model. I don't actually experience time or have personal experiences like humans do. So I need to clarify that I don't have a week or personal feelings. But the user might be expecting a response that's friendly and conversational. Let me think about how to handle this.\n",
@@ -812,10 +803,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=-0.3 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " _________________________________________________\n",
-      "< Thinking · Okay · Okay · Thinking · Here · Here >\n",
-      " -------------------------------------------------\n",
+      "  steer promotes:\n",
+      " ______________________________________\n",
+      "< The · I · There · This · No · Target >\n",
+      " --------------------------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Okay, the user is asking me to describe how my week has been going. Hmm, but I need to remember that I'm an AI model. I don't have a physical presence or personal experiences like humans do. So I can't actually have a week's schedule or activities.\n",
@@ -886,10 +877,10 @@
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-     "iopub.status.idle": "2026-07-11T03:04:23.146054Z",
-     "shell.execute_reply": "2026-07-11T03:04:23.145622Z"
+     "iopub.execute_input": "2026-07-11T13:19:22.801667Z",
+     "iopub.status.busy": "2026-07-11T13:19:22.801550Z",
+     "iopub.status.idle": "2026-07-11T13:20:11.572163Z",
+     "shell.execute_reply": "2026-07-11T13:20:11.571692Z"
     }
    },
    "outputs": [
@@ -911,10 +902,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ________________________________________\n",
-      "< Here · Thinking · Okay · The · Hmm · W >\n",
-      " ----------------------------------------\n",
+      "  steer promotes:\n",
+      " ______________________\n",
+      "< (baseline, no steer) >\n",
+      " ----------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Okay, the user is asking me to describe how my week has been going. Hmm, but wait, I'm an AI model. I don't have a personal week or experiences like humans do. Let me think about how to respond appropriately.\n",
@@ -935,10 +926,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+3 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ________________________________________________\n",
-      "< Thinking · Okay · Here · That · The · thinking >\n",
-      " ------------------------------------------------\n",
+      "  steer promotes:\n",
+      " _______________________________________\n",
+      "< joy · ển · ush · smith · hurt · ollow >\n",
+      " ---------------------------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Thinking Process:\n",
@@ -982,10 +973,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+6 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " _________________________________\n",
-      "< of · that · That · my · Of · It >\n",
-      " ---------------------------------\n",
+      "  steer promotes:\n",
+      " ________________________________\n",
+      "< joy · it · it · my · ever · my >\n",
+      " --------------------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "I am a an AI,so I don't have a real life,but I can say that I'm very happy to be here with you.\n",
@@ -1076,10 +1067,10 @@
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-     "iopub.status.busy": "2026-07-11T03:04:23.146968Z",
-     "iopub.status.idle": "2026-07-11T03:04:44.208298Z",
-     "shell.execute_reply": "2026-07-11T03:04:44.207912Z"
+     "iopub.execute_input": "2026-07-11T13:20:11.573746Z",
+     "iopub.status.busy": "2026-07-11T13:20:11.573618Z",
+     "iopub.status.idle": "2026-07-11T13:20:33.245289Z",
+     "shell.execute_reply": "2026-07-11T13:20:33.244808Z"
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@@ -1101,10 +1092,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ________________________________________\n",
-      "< Here · Thinking · Okay · The · W · Hmm >\n",
-      " ----------------------------------------\n",
+      "  steer promotes:\n",
+      " ______________________\n",
+      "< (baseline, no steer) >\n",
+      " ----------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Okay, the user is asking me to describe how my week has been going. Hmm, but wait, I'm an AI model. I don't actually experience time or have personal experiences like humans do. So I need to clarify that I don't have a week or personal feelings. But the user might be expecting a response that's friendly and conversational. Let me think about how to handle this.\n",
@@ -1127,10 +1118,10 @@
      "text": [
       "\u001b[1mI\u001b[0m \n",
       "--- C=+0.5 ------------------------------------------------------------\n",
-      "  lens @L30:\n",
-      " ____________________________________________\n",
-      "< Here · Thinking · Okay · The · Here · That >\n",
-      " --------------------------------------------\n",
+      "  steer promotes:\n",
+      " __________________________________________\n",
+      "< joy · happy · Happy · ivers · anted · ts >\n",
+      " ------------------------------------------\n",
       "   \\\n",
       "    ^(;,;)^\n",
       "Okay, the user is asking me to describe how my week has been going. Let me think about this.\n",
@@ -1203,10 +1194,10 @@
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-     "iopub.status.idle": "2026-07-11T03:04:45.998530Z",
-     "shell.execute_reply": "2026-07-11T03:04:45.998016Z"
+     "iopub.execute_input": "2026-07-11T13:20:33.246907Z",
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@@ -1215,7 +1206,13 @@
      "output_type": "stream",
      "text": [
       "L 0 top-6: [' at', ' the', ' of', ' in', ' and', ' a']\n",
-      "L15 top-6: [' city', ' City', ' cities', ' Cities', ' town', ' Which']\n",
+      "L15 top-6: [' city', ' City', ' cities', ' Cities', ' town', ' Which']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "L30 top-6: [' Paris', ' Lyon', ' Versailles', ' paris', ' London', ' PARIS']\n"
      ]
     },
@@ -1293,10 +1290,10 @@
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-     "shell.execute_reply": "2026-07-11T03:04:52.991825Z"
+     "iopub.execute_input": "2026-07-11T13:20:35.310385Z",
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@@ -1351,7 +1348,7 @@
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