{
  "planned": 30,
  "successful": 30,
  "failures": [],
  "design": "Fresh seeds 20261201\u201320261230; original generating bank fixed; 1600 training/800 test per attempt.",
  "selection": "Two feasible starts; up to three within-domain best-swap rounds per start; common 1024 fitted-model design cases; exact response-pattern finalist loss on a 0.05 target grid.",
  "comparisons": {
    "constrained": {
      "target_rmse": {
        "mean": -0.030784495357392312,
        "sd": 0.01924000468290284,
        "min": -0.06731064078838822,
        "max": 0.013450725430055932,
        "monte_carlo_se": 0.003512728190443639,
        "mean_95_mc_t_interval": [
          -0.03796883117724281,
          -0.023600159537541812
        ],
        "negative_count": 27,
        "positive_count": 3
      },
      "rmse": {
        "mean": -0.05268892675043168,
        "sd": 0.016336759914297696,
        "min": -0.08219564577443206,
        "max": -0.011993218678850226,
        "monte_carlo_se": 0.0029826706405356707,
        "mean_95_mc_t_interval": [
          -0.05878917315717417,
          -0.04658868034368919
        ],
        "negative_count": 30,
        "positive_count": 0
      },
      "minimum_target_information": {
        "mean": -0.6494866529422619,
        "sd": 0.22672529490476026,
        "min": -1.2576532490524341,
        "max": -0.17557425119060888,
        "monte_carlo_se": 0.041394186125449435,
        "mean_95_mc_t_interval": [
          -0.7341472694179894,
          -0.5648260364665345
        ],
        "negative_count": 30,
        "positive_count": 0
      }
    },
    "item_total": {
      "target_rmse": {
        "mean": -0.019371092905618982,
        "sd": 0.022573577608355634,
        "min": -0.07145736951754639,
        "max": 0.025156607339420378,
        "monte_carlo_se": 0.004121352553229966,
        "mean_95_mc_t_interval": [
          -0.027800205313164208,
          -0.010941980498073756
        ],
        "negative_count": 25,
        "positive_count": 5
      },
      "rmse": {
        "mean": 0.016426007420458317,
        "sd": 0.01548568490205257,
        "min": -0.02949294813589748,
        "max": 0.044249358594862453,
        "monte_carlo_se": 0.002827286313090457,
        "mean_95_mc_t_interval": [
          0.010643557646129634,
          0.022208457194787
        ],
        "negative_count": 2,
        "positive_count": 28
      },
      "minimum_target_information": {
        "mean": 1.8383576126211711,
        "sd": 0.3962672612357218,
        "min": 1.169253280506553,
        "max": 2.8764446526312435,
        "monte_carlo_se": 0.0723481725931991,
        "mean_95_mc_t_interval": [
          1.6903889854794274,
          1.986326239762915
        ],
        "negative_count": 0,
        "positive_count": 30
      }
    },
    "content_matched": {
      "target_rmse": {
        "mean": -0.03198110995034899,
        "sd": 0.02662350501628139,
        "min": -0.0830026426728428,
        "max": 0.014698059543951092,
        "monte_carlo_se": 0.0048607647524230874,
        "mean_95_mc_t_interval": [
          -0.04192249010543851,
          -0.02203972979525946
        ],
        "negative_count": 26,
        "positive_count": 4
      },
      "rmse": {
        "mean": 0.0029896355221449918,
        "sd": 0.01586655016154184,
        "min": -0.024102315033497468,
        "max": 0.05395438578526246,
        "monte_carlo_se": 0.0028968224777545675,
        "mean_95_mc_t_interval": [
          -0.0029350316773549523,
          0.008914302721644935
        ],
        "negative_count": 18,
        "positive_count": 12
      },
      "minimum_target_information": {
        "mean": 1.8167643394181383,
        "sd": 0.9789326556084508,
        "min": -3.907985046680551e-14,
        "max": 3.376652860790461,
        "monte_carlo_se": 0.17872783258506156,
        "mean_95_mc_t_interval": [
          1.4512248783410393,
          2.1823038004952373
        ],
        "negative_count": 1,
        "positive_count": 29
      }
    }
  },
  "mean_target_rmse": {
    "scoring_loss": 0.40183769218580095,
    "full": 0.2582973019337634,
    "constrained": 0.43262218754319337,
    "item_total": 0.42120878509142,
    "content_matched": 0.43381880213615
  },
  "mean_fitted_model_mse_gain_over_maximin": 0.024250671163472872,
  "notes": [
    "Heuristic search, not a globally optimal scoring-loss form.",
    "All loss calculations for selection use fitted training parameters, not generating parameters or heldout true theta.",
    "Finalist pattern summation is exact; theta integration is trapezoidal quadrature.",
    "Monte Carlo intervals apply only to this fixed-bank simulation; no claim of clinical or cross-bank generality."
  ],
  "input_sha256": {
    "whitepapers/fitted-demo/scoring-loss/attempts.json": "de6b0b8f9cdf711a653e1339d51d95d26bd956b79d4bb80fb1e8d7c605e85858",
    "scripts/fitted-short-form-demo/scoring_loss.py": "d511fabe9b2f5915317a1cc8c6946e9c6a4738102f586296ff673bd8098728fa",
    "scripts/fitted-short-form-demo/evaluate_scoring_loss.py": "42776b30b841f39338950a3ed97512715848209384d7bfe63cd2264109c3fa61",
    "scripts/fitted-short-form-demo/prepare_scoring_loss.R": "cb2f27f60cb5f8f88febba1f8ec6244ffb2d39a32f55b7775d007f591cfa5bc9",
    "whitepapers/fitted-demo/scoring-loss/20261201/parameters.csv": "0602a740f15ed82ce5794f3df2a058e989323f59f06dc3cf105defa30d45730c",
    "whitepapers/fitted-demo/scoring-loss/20261201/baseline-results.json": "a767cc438f50d7a084a73c56fa8bd08a551b793332b6aed1195facadf2f5d356",
    "whitepapers/fitted-demo/scoring-loss/20261201/baseline-scores.csv": "07d75b1cab9c7377843e6f5b0a21b1db528abe29b781ad7cc20195d09685d125",
    "whitepapers/fitted-demo/scoring-loss/20261201/test-responses.csv": "9953032da6b6fd8d117e9e2906f9641798b96adbb0db70f28491f11b76c5c6cb",
    "whitepapers/fitted-demo/scoring-loss/20261202/parameters.csv": "15709f8f67772f2b0a0eda98431fbb75a3f28da18bb4e2e7d177f14f99cf309f",
    "whitepapers/fitted-demo/scoring-loss/20261202/baseline-results.json": "c31122e052dc967f456f802bcbda5c5f7795b1d80a6288b2ef00901f65ec772a",
    "whitepapers/fitted-demo/scoring-loss/20261202/baseline-scores.csv": "2d3e6fed34ef4b7cb3fc0ee4afd320abf86d10a9ef9fa0f00fb6d94efee11d0e",
    "whitepapers/fitted-demo/scoring-loss/20261202/test-responses.csv": "6929dfe26b108a2e18b49da33e0e437c02e87849765d963b09fc20455c07793f",
    "whitepapers/fitted-demo/scoring-loss/20261203/parameters.csv": "11f1f6685cb5774109bc493ac6ccd54f524f53b2a93a18d09e496d081e775cdc",
    "whitepapers/fitted-demo/scoring-loss/20261203/baseline-results.json": "aea47a6ccdb21600801235751762aa8848a5b7df3baa42d0f193a023179a5917",
    "whitepapers/fitted-demo/scoring-loss/20261203/baseline-scores.csv": "c2e9531c24f29dc053fb7c4ddbeacfe4e2c930336230e99b29163aceb6fcfef5",
    "whitepapers/fitted-demo/scoring-loss/20261203/test-responses.csv": "4592e8da31eb8484b53584e03ef1966e879db55d41c593dc2902f90279cefa82",
    "whitepapers/fitted-demo/scoring-loss/20261204/parameters.csv": "f121d11f31719a0b27205480a6b02e76167ed26613dd510ff3f7a9a3d617e2f2",
    "whitepapers/fitted-demo/scoring-loss/20261204/baseline-results.json": "9af716de9328055ccc07e7896b03996e47431b149f416d81a62850872250fe4f",
    "whitepapers/fitted-demo/scoring-loss/20261204/baseline-scores.csv": "78bdfb3c365275679baa6cd8c4e03b4fcd1d0c79cb45bbb42b52365469fb8a6a",
    "whitepapers/fitted-demo/scoring-loss/20261204/test-responses.csv": "fc96b20906d2ac294bc8d26a22f2b2ec9633a010e9daf6948e8e64779cb8995e",
    "whitepapers/fitted-demo/scoring-loss/20261205/parameters.csv": "3cdd9452f0e9f9754f696dfb70dcff9fc15df58053f7176f7e00e5171704a115",
    "whitepapers/fitted-demo/scoring-loss/20261205/baseline-results.json": "60d4818645be688b720925c9dd6ce4364ebf4302bb1875a0e63f0114b3bb22d4",
    "whitepapers/fitted-demo/scoring-loss/20261205/baseline-scores.csv": "e39bd97066bc176ab7469a63462468138b1d576b709f30ec975a802165bd1481",
    "whitepapers/fitted-demo/scoring-loss/20261205/test-responses.csv": "6966fcea5854b766e09df78ef84ce4e86e220d96cffe406fc6cfc7fdb0e88256",
    "whitepapers/fitted-demo/scoring-loss/20261206/parameters.csv": "1214df18b71a9dd78524cd6630ed5f9997ed5c8c87ea01b3855018d0acae5f9f",
    "whitepapers/fitted-demo/scoring-loss/20261206/baseline-results.json": "14d5a1f4d966705a3c2e978694abf8680fb3b05358f540efd880e5e7dd8ce8e8",
    "whitepapers/fitted-demo/scoring-loss/20261206/baseline-scores.csv": "20b898eefde33cbb8f4f1052ef00972afe694d4dd328bb7a368701455c3caef0",
    "whitepapers/fitted-demo/scoring-loss/20261206/test-responses.csv": "2b8ac4e39e7c0d810d42d25fd5140b71a3bfa23353de0359ec3847ca8b1eb187",
    "whitepapers/fitted-demo/scoring-loss/20261207/parameters.csv": "0f83f8399cc313379a1e430ed3f1d1924ad0d09518b0e223941d7931d8faee77",
    "whitepapers/fitted-demo/scoring-loss/20261207/baseline-results.json": "aaea84f81d5565a3b26b58050866f11ae70eed6a94bb2dc71dc957e3f395fdd5",
    "whitepapers/fitted-demo/scoring-loss/20261207/baseline-scores.csv": "d393a18d91444d2a2c5f8f3300fd6c4b1d1187958f6f3475709bd6036d4e404e",
    "whitepapers/fitted-demo/scoring-loss/20261207/test-responses.csv": "2110782802fd9e1bcccf960d2477846eb3e3c5544d702c4b07b3fa3438f68948",
    "whitepapers/fitted-demo/scoring-loss/20261208/parameters.csv": "a89dbb5919664d2e16383de5f6137cf8c24b9934e1200fa656314e9e52812ca6",
    "whitepapers/fitted-demo/scoring-loss/20261208/baseline-results.json": "ee3274d539c899e97787c96041306e8a3874c765604bcfe711e47fd0483e0975",
    "whitepapers/fitted-demo/scoring-loss/20261208/baseline-scores.csv": "b746b379ebb0ce89e27b06728fa3156843abf4d0e4e03e814fe4062a05538d35",
    "whitepapers/fitted-demo/scoring-loss/20261208/test-responses.csv": "da97b21c0e5d6ff93e2a40de7f2ddfc6f7cb2cbb7a9fb567f5f2429bf26e8edc",
    "whitepapers/fitted-demo/scoring-loss/20261209/parameters.csv": "acb2c4251c9bbb70b11055cd7ae0d3bdcd81f35e1c4e4882fda656f78b818464",
    "whitepapers/fitted-demo/scoring-loss/20261209/baseline-results.json": "dfee2f33bce19f5f2ecb23b5d5486ff338f0ce8c30a71c3b3e7906cf4002cdf7",
    "whitepapers/fitted-demo/scoring-loss/20261209/baseline-scores.csv": "2b42154271f71119117e701de6238a86491a90cd5518fa0e1d75a483f637d73f",
    "whitepapers/fitted-demo/scoring-loss/20261209/test-responses.csv": "9b030be1442befaf299a8827eb631ebcdd89cccf6241982c35a34bd58956ddd4",
    "whitepapers/fitted-demo/scoring-loss/20261210/parameters.csv": "c06192418862c6be10f66a88fc4811ce50dafcfbb9ac44c3f518ecc426c0b4ce",
    "whitepapers/fitted-demo/scoring-loss/20261210/baseline-results.json": "a9fc2ca1a414f0ec2aaa75376829b501e5590fb75f8e6519b523b03667d3ccbf",
    "whitepapers/fitted-demo/scoring-loss/20261210/baseline-scores.csv": "b3ad5b0e5431b2f68671aafd9d5514b54cdb1fd0333d7175f29920e01ba3f7b8",
    "whitepapers/fitted-demo/scoring-loss/20261210/test-responses.csv": "c5c2d4da3324e6e4a5d3fa194dd7c36d9217855bd871e7fb586ef073ae04a8cc",
    "whitepapers/fitted-demo/scoring-loss/20261211/parameters.csv": "d01010191df16af255bafd0b56d60283c69af0705519b90bc6df5f391807df2d",
    "whitepapers/fitted-demo/scoring-loss/20261211/baseline-results.json": "1e9a460e53c786acfee6e3d3ec76088ac1be78290fa9081705be8e84701d1ab5",
    "whitepapers/fitted-demo/scoring-loss/20261211/baseline-scores.csv": "df8325e01032096d616bbb9c4406a269e61b0860182706c1b0f2edda321583c9",
    "whitepapers/fitted-demo/scoring-loss/20261211/test-responses.csv": "0cba8be48ccd36d6c3bc0275cfa04433de15d9e078b90c5f43629c147e5e19aa",
    "whitepapers/fitted-demo/scoring-loss/20261212/parameters.csv": "831050589303f9e598af9fa64008e6fb2d739d3c1639eeb0a4ead7348557105a",
    "whitepapers/fitted-demo/scoring-loss/20261212/baseline-results.json": "f8e88162cad9c90d3db783c8eb6adba7f2fc5a9cb86d3f6aebeeec025babe403",
    "whitepapers/fitted-demo/scoring-loss/20261212/baseline-scores.csv": "a08b072a7f80d1b89837ff0b4a97ec27ecba2ad8e7c555f09033eb607d29b6cc",
    "whitepapers/fitted-demo/scoring-loss/20261212/test-responses.csv": "c4371b1cd234aa2f07cc5149cf51eea7e9bddbf5f52c163b25f3f205050ecce6",
    "whitepapers/fitted-demo/scoring-loss/20261213/parameters.csv": "81ffac8eaf0b86c072999c240116209742d26a36ac452c09c97134c58aaa5349",
    "whitepapers/fitted-demo/scoring-loss/20261213/baseline-results.json": "ba0c23ddeee7951943de2b8c1db962740448f1c3ce3af06fcfdfb89450c5d726",
    "whitepapers/fitted-demo/scoring-loss/20261213/baseline-scores.csv": "aa475dfe5114aa30b0e331fa5e2f254165bdeed3437992930d2e458c8dd6cf71",
    "whitepapers/fitted-demo/scoring-loss/20261213/test-responses.csv": "f7aa8ac18933c09600cabead6002af108ea648bf25c861449a42cc750e67de63",
    "whitepapers/fitted-demo/scoring-loss/20261214/parameters.csv": "661b5762d78c58b73a9201afddacdaca16623c605eaeaecf22d76ad93913e44e",
    "whitepapers/fitted-demo/scoring-loss/20261214/baseline-results.json": "73105e725c2871b294d0f23d1f8f0793af0e8c0f9010d06f09a2bc69e36e1cd2",
    "whitepapers/fitted-demo/scoring-loss/20261214/baseline-scores.csv": "0e860108be8d185dc34968a184b968f896d49d13d7eb57807c66795b024cfef2",
    "whitepapers/fitted-demo/scoring-loss/20261214/test-responses.csv": "d93b7389c2e30535f6587c9e27cacbcabc2cf4f165edb2bc4e34d39689cba55c",
    "whitepapers/fitted-demo/scoring-loss/20261215/parameters.csv": "0249e026298d5f2ee5291828084ec20a058ae6e39f4cd279f9985555ed16a40d",
    "whitepapers/fitted-demo/scoring-loss/20261215/baseline-results.json": "d8fe72411e60507d92947b16efb8851b3c93654730e77be1caf7c02ab4491ecd",
    "whitepapers/fitted-demo/scoring-loss/20261215/baseline-scores.csv": "6f60bdd2f821dc1fd2aae760c84d6f8cc20aa85de458a026670f4d9cb6f5808c",
    "whitepapers/fitted-demo/scoring-loss/20261215/test-responses.csv": "3ca072abe0f446d0532f44bd4c529955b48e74fd3d45e81c91d058c740a8293a",
    "whitepapers/fitted-demo/scoring-loss/20261216/parameters.csv": "eaae217b288922e9c8e4e70c55fc87208f5ca5b8fafdcec57f325504250a8583",
    "whitepapers/fitted-demo/scoring-loss/20261216/baseline-results.json": "41a67d213f91d3911ff72ed3d31536bdc44859c7cd099da4807803d024668f82",
    "whitepapers/fitted-demo/scoring-loss/20261216/baseline-scores.csv": "c7e6aa23b6c6b494f4a8485def75dfe14eb8972e33285dc633e47bd2b36a39fd",
    "whitepapers/fitted-demo/scoring-loss/20261216/test-responses.csv": "2c0a55d44a2814e4b068e538693aa44539ee7aa036388153942407094b5ce7e9",
    "whitepapers/fitted-demo/scoring-loss/20261217/parameters.csv": "8828c33c353265444d241e194e5dbbf05dd534c765852f543335876b15f202ba",
    "whitepapers/fitted-demo/scoring-loss/20261217/baseline-results.json": "b5778159bef1eccce3a4074a25231a8ffe5efe4f453b77324c68f42a1fc52e46",
    "whitepapers/fitted-demo/scoring-loss/20261217/baseline-scores.csv": "857dc2b448108a96bb831f340fa38940321ba39885b398abbf3969c3ab6d017d",
    "whitepapers/fitted-demo/scoring-loss/20261217/test-responses.csv": "6affeaacaadde32ec4d887eed1166200dca98ec46f427acee6c36911e3d18c05",
    "whitepapers/fitted-demo/scoring-loss/20261218/parameters.csv": "375c3174f93aa1730f9ca52e9080f5dc72bdcfe6016366690a01df302261666a",
    "whitepapers/fitted-demo/scoring-loss/20261218/baseline-results.json": "083858170d715060583634314ab41c9390705edd6a55a3384856e56e3692d8b6",
    "whitepapers/fitted-demo/scoring-loss/20261218/baseline-scores.csv": "a693f229e440b786513c48d7fe7311bd3b105bf08e8d4a29d45035b7e954b77c",
    "whitepapers/fitted-demo/scoring-loss/20261218/test-responses.csv": "9fff284375b6cf61aac1f1396e8156f6f29660270f76e912f691e654131b254d",
    "whitepapers/fitted-demo/scoring-loss/20261219/parameters.csv": "19479a90a5e8d1e72d41f56d2cffb4f94a51cffde65c648d46bbc878def796cc",
    "whitepapers/fitted-demo/scoring-loss/20261219/baseline-results.json": "35b9d67123f3bfff3c0831f75756b801d876f331308cefcfde0c8587c4348008",
    "whitepapers/fitted-demo/scoring-loss/20261219/baseline-scores.csv": "c3d0dfff7f9c5780f2ea60b4f1a0e5db816b3e37365aa7ed893a5305d10515a4",
    "whitepapers/fitted-demo/scoring-loss/20261219/test-responses.csv": "985006edab6994dc03ec7361019095513aaa4061773f7bce86a3caeafa9b39fa",
    "whitepapers/fitted-demo/scoring-loss/20261220/parameters.csv": "8fa674c9837b1809c09eaba71531a66609a2538b3c48e0b4357408729bf385c3",
    "whitepapers/fitted-demo/scoring-loss/20261220/baseline-results.json": "76483b1f1314b21cf7fbcff7116967fa76ec23a55bcdc7f456be6ab69c2e99f2",
    "whitepapers/fitted-demo/scoring-loss/20261220/baseline-scores.csv": "c927361b70e93ac3febe266da28d3f089aea55d45c095a05de1a65eee3588a42",
    "whitepapers/fitted-demo/scoring-loss/20261220/test-responses.csv": "91d3a5393027277170aa9e90619406756030c32d9cf96ce09132ea6e5645f3c2",
    "whitepapers/fitted-demo/scoring-loss/20261221/parameters.csv": "98c072ccd747d80049b68a21ca0a838bff9b44d8b077fd77b7995145660865be",
    "whitepapers/fitted-demo/scoring-loss/20261221/baseline-results.json": "a31f3b70c895fc71a5c2cfed2ad4f2d2be06c016fe5b8283892e40c2e5d78d2a",
    "whitepapers/fitted-demo/scoring-loss/20261221/baseline-scores.csv": "ff22a0c5e8bac4641a1213140c0239077c42480aeca08c4acfdd697214f4f520",
    "whitepapers/fitted-demo/scoring-loss/20261221/test-responses.csv": "d336c39853e2ab8eacead8fa596933d6e0c81cca175a317681d290f63015569c",
    "whitepapers/fitted-demo/scoring-loss/20261222/parameters.csv": "4e66dda17148e622d33fcc6102b3c3ec0e7a505be15d20cd76ab19007477b5b7",
    "whitepapers/fitted-demo/scoring-loss/20261222/baseline-results.json": "384a518332c4020890e38d919a8ea9893f7721a6bf95f7fea3d8da2e8446c1c6",
    "whitepapers/fitted-demo/scoring-loss/20261222/baseline-scores.csv": "2e81d477d1d8da8665c080815aa7ce7cc1257c356fa568c95bd28d06e15aa7df",
    "whitepapers/fitted-demo/scoring-loss/20261222/test-responses.csv": "5267df62d905f715118aba5354b04f03e71e0d68f540bc1d3a4f837927a84fc3",
    "whitepapers/fitted-demo/scoring-loss/20261223/parameters.csv": "afd7303e4398c48107764abedb66a67286028eb03e840e0cc871176296d978ca",
    "whitepapers/fitted-demo/scoring-loss/20261223/baseline-results.json": "b69d70330cbf24d852d073fcfaec2ec9784f95839d139ec5277745f9ee657ea2",
    "whitepapers/fitted-demo/scoring-loss/20261223/baseline-scores.csv": "33a68891ee1410c64ca78c47c364402d2eb9ab9b5975dd4df4e741beea5d468f",
    "whitepapers/fitted-demo/scoring-loss/20261223/test-responses.csv": "3a4e339bc014b2934d68454ee210487b99692d5d1eddcb825d5bf02db133684b",
    "whitepapers/fitted-demo/scoring-loss/20261224/parameters.csv": "c4cb35a3ea17b389d5ccd7789dce4be17977458ca5faba043cf3e747268a2d11",
    "whitepapers/fitted-demo/scoring-loss/20261224/baseline-results.json": "2707f1caa1fc451fcf4751f1d0e6703e26a5469690172766b4f227da1f5f6fbe",
    "whitepapers/fitted-demo/scoring-loss/20261224/baseline-scores.csv": "3c18c8e78cc921408a859878a53dec282943c8b3bc60d06f5c3f356faaac9dda",
    "whitepapers/fitted-demo/scoring-loss/20261224/test-responses.csv": "f6b3d6c903a7a1a53613e45db8d94891103e108282996e99e17f0607d3439dae",
    "whitepapers/fitted-demo/scoring-loss/20261225/parameters.csv": "cbb96f034b4ea6692de759f0a9b667394c4b1f2062347979d55cd615afaad1f9",
    "whitepapers/fitted-demo/scoring-loss/20261225/baseline-results.json": "5a082d6de22df0122806bdc3d81f1631d66283271f6e2705e19935c9581af085",
    "whitepapers/fitted-demo/scoring-loss/20261225/baseline-scores.csv": "914f0a0b9e90350b5eeebda3e9451cb291416f0edb5a2bbf3062543ac87fe68f",
    "whitepapers/fitted-demo/scoring-loss/20261225/test-responses.csv": "c48aa056ff154289f9fff49025f9b1a62889302508b616c05b508f45c2abe8c8",
    "whitepapers/fitted-demo/scoring-loss/20261226/parameters.csv": "40d7a1c087fca477d94e82afd54b5961fc44e25b54c7d6323221a6a1fc271d15",
    "whitepapers/fitted-demo/scoring-loss/20261226/baseline-results.json": "75bb45b4c7e0f5daa695f5c0123a9221913a7a69ee61472ca4c7f1af8441688f",
    "whitepapers/fitted-demo/scoring-loss/20261226/baseline-scores.csv": "58ab876bf22375c5b87e2751202c14115bd91e29e0dd8c3b88eed6fb3105224a",
    "whitepapers/fitted-demo/scoring-loss/20261226/test-responses.csv": "86a33075a29f45c653e6cf51440e440b354fa6c43fcd746f4ebcce96eba8bfa2",
    "whitepapers/fitted-demo/scoring-loss/20261227/parameters.csv": "c3baa5df2868ad6679a2d2214c2459992a673d3bbfeffcad1556bd67cd15664f",
    "whitepapers/fitted-demo/scoring-loss/20261227/baseline-results.json": "6bfa26af884adfda9edf84a214f89132387050d7b1f71e7fe02115b215c3c3c9",
    "whitepapers/fitted-demo/scoring-loss/20261227/baseline-scores.csv": "b2e586aa3eeca5551cb96aeb7c34a61c4c85b905ed39c5d82db81dd1479a1b18",
    "whitepapers/fitted-demo/scoring-loss/20261227/test-responses.csv": "1cb83319a3cca1d699289fb77b7189d6d64a236ba2e5b13d2a148d1d67cd6676",
    "whitepapers/fitted-demo/scoring-loss/20261228/parameters.csv": "7bff90c549106dbd351a89dcf0cf4727301449300b7a93c158cc2286bd9a59c8",
    "whitepapers/fitted-demo/scoring-loss/20261228/baseline-results.json": "f98a5d82df1d24d2ad11dbbdd16e70501550eb2ed8406bd9c4db81dc7a1ee366",
    "whitepapers/fitted-demo/scoring-loss/20261228/baseline-scores.csv": "00b627aa26f4a9346053d44af4c8171ea2b274d3057d71ed44a42ba07f5580e3",
    "whitepapers/fitted-demo/scoring-loss/20261228/test-responses.csv": "f940e78616b466cb5eb90a380278bdd3214aea0215b8c7de0c00ee50b5920cdd",
    "whitepapers/fitted-demo/scoring-loss/20261229/parameters.csv": "40f488e93b8f6de7ce064b56d0a4a25fc767b910ce7f938aabdb74815f8e6c47",
    "whitepapers/fitted-demo/scoring-loss/20261229/baseline-results.json": "e6a11efe933632a13d8254c036b59f824d058a4e863802e13bc3a990cf9c2930",
    "whitepapers/fitted-demo/scoring-loss/20261229/baseline-scores.csv": "26505bfb580b446b236e7f61884eb044cfe69ca751cfc1414243ca1ea77e2f51",
    "whitepapers/fitted-demo/scoring-loss/20261229/test-responses.csv": "47839667c4641387b7eb90f992363bbd596312587671bfeb5dde4d8b5a77c1ee",
    "whitepapers/fitted-demo/scoring-loss/20261230/parameters.csv": "81cf06f8bcf556fa79f2754f9426427ece8d9835ad3e46556b020728495e8c33",
    "whitepapers/fitted-demo/scoring-loss/20261230/baseline-results.json": "e0fd9c9028a8e11676a28da9a221d2e73df610977135d06d474fa2731caad2b4",
    "whitepapers/fitted-demo/scoring-loss/20261230/baseline-scores.csv": "d6bd6d568abee771c1db33474dc436309dbdddd9e00aa777c7cef959bf879b83",
    "whitepapers/fitted-demo/scoring-loss/20261230/test-responses.csv": "100cb53bb60b05a100f40e4825316442c3c0558b5f229388a938bd4b9165aac5"
  }
}