{"id":"W4398447939","doi":"10.7910/dvn/ii5jzg/ep4nlq","title":"MSP_F_100_NFL_4_19.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":[0.0003217885,0.0003891831,0.0008008425,0.0002721979,0.0001234694,0.0001966104,0.0009335919,0.000321863,0.0893801],"category_scores_gemma":[0.0001305362,0.0004553458,0.0002363627,0.0002821086,0.00008116708,0.0002788486,0.000381969,0.0005462208,0.6787434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000888989,"about_ca_system_score_gemma":0.00006043817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009426995,"about_ca_topic_score_gemma":0.00009610406,"domain_scores_codex":[0.9978253,0.000007522308,0.0008418764,0.0008116301,0.0000899021,0.0004237527],"domain_scores_gemma":[0.9976284,0.00002424371,0.0006528112,0.001421578,0.00003076759,0.0002422371],"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":[0.00001383456,0.00004668994,0.0001607167,0.0001117317,0.000102025,0.00008296506,0.00001475766,0.0000230218,1.455629e-7,0.0024732,0.9969242,0.00004670762],"study_design_scores_gemma":[0.0003177796,0.00004548002,0.0001761227,0.000029219,0.00003847199,0.000006715425,0.000009343949,0.0005612971,0.000001132424,0.0003962519,0.9978628,0.0005553769],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001026875,0.00001781955,0.00008501594,0.00004021561,0.001328406,0.0001786705,0.9956011,0.0000433252,0.002695224],"genre_scores_gemma":[0.00009521808,0.00275415,0.0001025285,0.001738603,0.000745413,0.00001672109,0.9933271,0.00004023585,0.001180046],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5893633,"threshold_uncertainty_score":0.9997898,"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."}}