{"id":"W4398593428","doi":"10.7910/dvn/ii5jzg/yzzz19","title":"MSP_F_100_NFL_4_27.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":"Physics","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":[0.0003229892,0.0003901289,0.0008037025,0.0002719667,0.0001235152,0.0001983741,0.0009358265,0.0003230313,0.08970409],"category_scores_gemma":[0.0001296581,0.0004561679,0.0002386438,0.000277742,0.00008162993,0.0002801745,0.0003830096,0.0005485408,0.6758494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009027989,"about_ca_system_score_gemma":0.00006021728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009167069,"about_ca_topic_score_gemma":0.00009328639,"domain_scores_codex":[0.9978177,0.000007549261,0.000844662,0.0008147456,0.00009022605,0.0004251027],"domain_scores_gemma":[0.9976168,0.00002436491,0.0006551621,0.001428257,0.00003054282,0.0002448336],"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":[0.00001377111,0.00004635811,0.0001573215,0.0001119942,0.0001011956,0.00008540962,0.00001488286,0.00002293704,1.51885e-7,0.002482084,0.996918,0.00004588997],"study_design_scores_gemma":[0.0003164354,0.00004624276,0.0001788357,0.00002960517,0.00003819755,0.000006850294,0.000009569429,0.0005623985,0.000001164879,0.00039535,0.9978591,0.0005562371],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001064737,0.00001684015,0.00008299504,0.00003998127,0.001341641,0.0001785663,0.995497,0.00004341821,0.002788875],"genre_scores_gemma":[0.00009870735,0.002720636,0.0001006066,0.001746829,0.0007610122,0.00001665804,0.9933089,0.00004031523,0.001206347],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5861453,"threshold_uncertainty_score":0.999789,"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."}}