{"id":"W4398356572","doi":"10.7910/dvn/ii5jzg/7qssuu","title":"MSP_F_100_NFL_4_4.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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00120025,0.002459363,0.001523958,0.004733423,0.0008546358,0.003862243,0.003216479,0.002688203,0.3627262],"category_scores_gemma":[0.009930205,0.0009637314,0.001326314,0.008255346,0.0006239878,0.002447607,0.003081755,0.001795763,0.349009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001794054,"about_ca_system_score_gemma":0.002274524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02728485,"about_ca_topic_score_gemma":0.03838148,"domain_scores_codex":[0.9986197,0.0002003591,0.0001678156,0.0003196509,0.0003251504,0.0003674012],"domain_scores_gemma":[0.9964498,0.0008829751,0.0003699782,0.0007765344,0.001071641,0.0004489862],"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":[0.00002573205,0.00001039695,0.0003493375,0.0002714439,0.00001009619,0.000006111758,0.00001053262,0.00007751938,0.00002922371,0.0002656335,0.9980913,0.0008526235],"study_design_scores_gemma":[0.0003162169,0.0000193528,0.003424729,0.0003213152,0.00001616515,0.00003112166,0.00009200394,0.0002545131,0.0002337834,0.001215096,0.9940432,0.00003260384],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004122388,0.00002024425,0.00002003426,0.00005334437,0.00001820036,0.000005052087,0.9990432,0.0002273105,0.0005713831],"genre_scores_gemma":[0.0003096876,0.00003845623,0.0001488929,0.00007011383,0.00001565972,0.00006095099,0.9979499,0.0001524603,0.001253744],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6372738,"threshold_uncertainty_score":0.9089937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03145694927339215,"score_gpt":0.2131536742860324,"score_spread":0.1816967250126402,"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."}}