{"id":"W4398598153","doi":"10.7910/dvn/ii5jzg/ptktwx","title":"MSP_F_20_NFL_4_22.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":[0.0003038968,0.0003755227,0.0007696471,0.0002603483,0.0001182612,0.000191398,0.0009019854,0.0003119333,0.08716171],"category_scores_gemma":[0.0001188119,0.000437905,0.0002287481,0.0002641034,0.00007588616,0.0002638468,0.000362873,0.0005294136,0.6799459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008524201,"about_ca_system_score_gemma":0.00005696682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008194479,"about_ca_topic_score_gemma":0.00008312365,"domain_scores_codex":[0.997903,0.000007081141,0.000808462,0.0007853041,0.00008648748,0.0004096307],"domain_scores_gemma":[0.9977099,0.00002229277,0.0006261806,0.001378061,0.00002838716,0.0002351278],"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":[0.00001348663,0.00004511422,0.0001381125,0.0001066514,0.00009701216,0.00008104614,0.00001475319,0.00002074904,1.577367e-7,0.002578351,0.9968575,0.00004711877],"study_design_scores_gemma":[0.0003052302,0.00004513058,0.0001548909,0.00002838378,0.00003730403,0.000006547049,0.000009250853,0.0005776781,0.000001175302,0.0004065831,0.9978931,0.0005347112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000009605061,0.00001599685,0.00009038481,0.00003878576,0.001285817,0.0001745845,0.9954848,0.00004135679,0.002858716],"genre_scores_gemma":[0.00009020969,0.002595858,0.000105554,0.001749149,0.0007372072,0.00001637827,0.9934838,0.00003886353,0.001183023],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5927842,"threshold_uncertainty_score":0.9998073,"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."}}