{
  "schemaVersion": 1,
  "lessonId": "operations-06",
  "contentHash": "28078c290e10a6e46e86fb9fd10cee8c44f9b8df69443bbdb51da4f712a9af8c",
  "executedAt": "2026-10-20T10:51:48.399230+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Overall MSE: 0.409 high-slice MSE: 4.0 release: False\nTwo-bin input total variation: 0.4 (requires investigation; not an automatic retraining order)\nOverall MSE: 0.409 high-slice MSE: 4.0 release: False\nTwo-bin input total variation: 0.4 (requires investigation; not an automatic retraining order)\nAggregate-only acceptance would miss the high-slice failure.\nUnweighted slice average: 2.005\nNew population MSE: 3.601\nPASS: operations-06\n",
  "notebookCells": [
    {
      "sourceHash": "089bfce4171e1ac95e49c00820048784df3087a6439dde21b361a613be62c727",
      "outputs": []
    },
    {
      "sourceHash": "72a94428e15a07f5cb059304ff9957e725d0d5787559ef88223a8cbc060f0afa",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall MSE: 0.409 high-slice MSE: 4.0 release: False\n",
            "Two-bin input total variation: 0.4 (requires investigation; not an automatic retraining order)\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "0559249d42b548b0965f5d981fe340c8e18ffa884b8a1c05efde84298bf09a7d",
      "outputs": []
    },
    {
      "sourceHash": "6c41862fe647a6e9fe67ee370c2e7a05dd40849973e83b552884971becc7cef3",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall MSE: 0.409 high-slice MSE: 4.0 release: False\n",
            "Two-bin input total variation: 0.4 (requires investigation; not an automatic retraining order)\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "13ea9d76109e6d1cecd474010724de59bcc9913b380d7a632ae9474b787590d7",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "b58c74ffb84767d80086c606c0cae9783535bb9b143182a25dd0f0239f269ee1",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aggregate-only acceptance would miss the high-slice failure.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "ff10214acd0c29e03cfcc68327dde44864b42a7c359cc907828b116eb5a55b93",
      "outputs": []
    },
    {
      "sourceHash": "36250f5b8d99a84802fafdc0317bb92fb7ec6a4d1bf4e04933a28a881129512b",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unweighted slice average: 2.005\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "f0611220d39795e9ba084de7afd192318e41fa403a52ed898c3e20312a08d91a",
      "outputs": []
    },
    {
      "sourceHash": "62d5875d2fadf131d6092419903b87da38e603096acfe30bf7309729e7afe281",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New population MSE: 3.601\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}