{"id":"W4398450098","doi":"10.7910/dvn/ii5jzg/178hk8","title":"MSP_F_100_NFL_4_24.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":[0.0003212279,0.0003897929,0.0008024074,0.0002710897,0.0001217546,0.0001968885,0.0009291333,0.0003221073,0.08579193],"category_scores_gemma":[0.00012877,0.0004559566,0.0002385601,0.0002763658,0.00008014285,0.0002771741,0.0003813211,0.0005456622,0.6737385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009015803,"about_ca_system_score_gemma":0.0000594562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009083585,"about_ca_topic_score_gemma":0.00009032407,"domain_scores_codex":[0.9978216,0.000007537295,0.0008431093,0.0008134823,0.00009000868,0.0004242362],"domain_scores_gemma":[0.9976221,0.00002451866,0.0006531197,0.001425269,0.00003049313,0.0002444758],"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":[0.00001453437,0.00004625728,0.0001910995,0.0001118795,0.0001011113,0.00008537682,0.00001472059,0.0000240381,1.434441e-7,0.002499008,0.9968695,0.00004231613],"study_design_scores_gemma":[0.0003219398,0.00004601158,0.0002117003,0.00002957402,0.00003817192,0.000006855125,0.000009404748,0.0005760482,0.000001113652,0.0003891659,0.9978142,0.0005558192],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000117132,0.00001670763,0.00008218976,0.00003856834,0.001336609,0.0001798993,0.9956705,0.00004450498,0.002619332],"genre_scores_gemma":[0.0001102952,0.002729581,0.00008928392,0.001729836,0.0007585438,0.00001665031,0.9933596,0.00004102957,0.001165229],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5879466,"threshold_uncertainty_score":0.9997892,"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."}}