{"id":"W4398562985","doi":"10.7910/dvn/ii5jzg/v6k0dk","title":"MSP_F_20_NFL_4_43.xlsx","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Materials science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003038976,0.0003748009,0.0007658998,0.0002600818,0.0001182175,0.0001913287,0.0008999808,0.0003112351,0.08557358],"category_scores_gemma":[0.0001225456,0.0004373565,0.0002284729,0.0002638299,0.00007482764,0.0002662818,0.000362293,0.000527045,0.678654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008474921,"about_ca_system_score_gemma":0.00005604229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008173386,"about_ca_topic_score_gemma":0.00008331351,"domain_scores_codex":[0.9979066,0.000007080792,0.0008065422,0.0007846288,0.0000864079,0.0004087889],"domain_scores_gemma":[0.9977123,0.00002246981,0.0006251254,0.001377302,0.00002828208,0.0002345903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001322296,0.00004506662,0.0001361915,0.0001069589,0.00009706953,0.00008075056,0.00001486294,0.0000209107,1.485678e-7,0.002395184,0.9970453,0.00004435543],"study_design_scores_gemma":[0.0003008072,0.00004540439,0.0001545062,0.00002915039,0.00003723759,0.000006516042,0.000009163144,0.0005845163,9.918679e-7,0.0003934639,0.9979042,0.0005339956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000008887885,0.0000128832,0.00008979779,0.00003909129,0.001279082,0.0001739073,0.9949516,0.00004168163,0.003403091],"genre_scores_gemma":[0.00008621861,0.002566656,0.0001046696,0.001729476,0.0007301759,0.00001635522,0.9933265,0.0000388464,0.001401032],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5930804,"threshold_uncertainty_score":0.9998078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03266678251959321,"score_gpt":0.2127360703839758,"score_spread":0.1800692878643826,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}