{
  "schemaVersion": 1,
  "lessonId": "deployment-02",
  "contentHash": "6db9af3ac8d9c5f8b63bdbeff90da292a562996376bbb32f458317ee9d879ee8",
  "executedAt": "2026-10-20T10:50:24.147400+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "{\n  \"pretrained\": {\n    \"held_cross_entropy\": 0.9780182838439941,\n    \"held_brier\": 0.17108705639839172,\n    \"source_cross_entropy\": 0.5451400876045227\n  },\n  \"supervised\": {\n    \"held_cross_entropy\": 0.5554870367050171,\n    \"held_brier\": 0.012093935161828995,\n    \"source_cross_entropy\": 0.7904530763626099\n  },\n  \"teacher\": {\n    \"held_cross_entropy\": 0.5240296125411987,\n    \"held_brier\": 0.00022982103109825402,\n    \"source_cross_entropy\": 1.0046310424804688\n  }\n}\ncheckpoint source hash: f5fb50c38195b46766bee2edaa81a7ad28386523baa850c5294eb71b47f1f84c\n{\n  \"pretrained\": {\n    \"held_cross_entropy\": 0.9780182838439941,\n    \"held_brier\": 0.17108705639839172,\n    \"source_cross_entropy\": 0.5451400876045227\n  },\n  \"supervised\": {\n    \"held_cross_entropy\": 0.5554870367050171,\n    \"held_brier\": 0.012093935161828995,\n    \"source_cross_entropy\": 0.7904530763626099\n  },\n  \"teacher\": {\n    \"held_cross_entropy\": 0.5240296125411987,\n    \"held_brier\": 0.00022982103109825402,\n    \"source_cross_entropy\": 1.0046310424804688\n  }\n}\ncheckpoint source hash: f5fb50c38195b46766bee2edaa81a7ad28386523baa850c5294eb71b47f1f84c\nwrong-teacher held loss: 1.3151594400405884\nsource retention losses: [0.5451400876045227, 0.7904530763626099, 1.0046310424804688]\nsupervised source loss increase 0.24531298875808716 passes declared gate True\nteacher source loss increase 0.45949095487594604 passes declared gate False\nCandidate selected under the declared fixture budget: supervised\nPASS: deployment-02\n",
  "notebookCells": [
    {
      "sourceHash": "fd6acbf5e0b9e35ef3c60916c7a73821ccc5416782678362260e5e0c6cc2de84",
      "outputs": []
    },
    {
      "sourceHash": "8140256e8ebe3f75b10961567466d5d798ecf881dc9311258ebd67dc8027d8e7",
      "outputs": []
    },
    {
      "sourceHash": "f59cd9524c09f6c4616868404c530aba3c14dc43125f8e8adebfd676968c9e0e",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"pretrained\": {\n",
            "    \"held_cross_entropy\": 0.9780182838439941,\n",
            "    \"held_brier\": 0.17108705639839172,\n",
            "    \"source_cross_entropy\": 0.5451400876045227\n",
            "  },\n",
            "  \"supervised\": {\n",
            "    \"held_cross_entropy\": 0.5554870367050171,\n",
            "    \"held_brier\": 0.012093935161828995,\n",
            "    \"source_cross_entropy\": 0.7904530763626099\n",
            "  },\n",
            "  \"teacher\": {\n",
            "    \"held_cross_entropy\": 0.5240296125411987,\n",
            "    \"held_brier\": 0.00022982103109825402,\n",
            "    \"source_cross_entropy\": 1.0046310424804688\n",
            "  }\n",
            "}\n",
            "checkpoint source hash: f5fb50c38195b46766bee2edaa81a7ad28386523baa850c5294eb71b47f1f84c\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "c322e393bfd03e42ff5196920312bf49d411e30e2cc58ddd1b6d28492be03554",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"pretrained\": {\n",
            "    \"held_cross_entropy\": 0.9780182838439941,\n",
            "    \"held_brier\": 0.17108705639839172,\n",
            "    \"source_cross_entropy\": 0.5451400876045227\n",
            "  },\n",
            "  \"supervised\": {\n",
            "    \"held_cross_entropy\": 0.5554870367050171,\n",
            "    \"held_brier\": 0.012093935161828995,\n",
            "    \"source_cross_entropy\": 0.7904530763626099\n",
            "  },\n",
            "  \"teacher\": {\n",
            "    \"held_cross_entropy\": 0.5240296125411987,\n",
            "    \"held_brier\": 0.00022982103109825402,\n",
            "    \"source_cross_entropy\": 1.0046310424804688\n",
            "  }\n",
            "}\n",
            "checkpoint source hash: f5fb50c38195b46766bee2edaa81a7ad28386523baa850c5294eb71b47f1f84c\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "4170f6f3d699307bd2f1e3fd15960ed9a3484bb322a9395f56daaac5c479dc28",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "1dbf3c99eaf4d0884c151f9059f9338ae53471e0792076204687e5bf35bc721c",
      "outputs": []
    },
    {
      "sourceHash": "d5571cc1cd097df6df04dfb61a4cc7f6da0759f5ef7562ed5ac133489b13343e",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "wrong-teacher held loss: 1.3151594400405884\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "b1921e39d5f7dacf8a0aba203f65c7f1ed07874a8cbb15dcb0c0bea990198258",
      "outputs": []
    },
    {
      "sourceHash": "705753c74567efd4db971668357db0b0ab8e8a81075adae64a49609bd59af14f",
      "outputs": []
    },
    {
      "sourceHash": "ccf143e4bdc5897087468ca7cf6dc8e9d5be9c63a088e805e42b1ec4a2296c22",
      "outputs": []
    },
    {
      "sourceHash": "8545c8d20ddfadeba0f1a5f16e2c28fc015ba8af5aeabb47802a3d40dfcfafea",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "source retention losses: [0.5451400876045227, 0.7904530763626099, 1.0046310424804688]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "aa8e09d5c15847dc982354fa81b20ee64f1e32aab9b496c155fcaa9082ac566e",
      "outputs": []
    },
    {
      "sourceHash": "558e2a96ba2af9e51e3f21b30f41f2b19ed1f139f6e4c32736a80fe31eaaf5f6",
      "outputs": []
    },
    {
      "sourceHash": "fa954c5e22dbbc4f6cb954a2f323b39e77060c00fa7fc687c48900cd86f8168b",
      "outputs": []
    },
    {
      "sourceHash": "7730d136218791bbefdd78c8a38ea4bd4b3a8a4e2ebe06ad4bb2d1c560f1c4d2",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "supervised source loss increase 0.24531298875808716 passes declared gate True\n",
            "teacher source loss increase 0.45949095487594604 passes declared gate False\n",
            "Candidate selected under the declared fixture budget: supervised\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}