{"id":"W4398500342","doi":"10.7910/dvn/ii5jzg/oh5epn","title":"MSP_F_100_NFL_4_47.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":[0.0003225528,0.0003900234,0.0008034616,0.0002719033,0.000124512,0.0001978615,0.0009349686,0.0003224164,0.08834209],"category_scores_gemma":[0.0001293174,0.0004560502,0.0002385824,0.0002779122,0.00008202354,0.0002791077,0.0003825129,0.0005474986,0.6751631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009034156,"about_ca_system_score_gemma":0.0000602037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009139437,"about_ca_topic_score_gemma":0.00009633684,"domain_scores_codex":[0.9978186,0.000007537691,0.0008443492,0.0008143882,0.0000902053,0.0004249127],"domain_scores_gemma":[0.9976179,0.0000243229,0.0006548863,0.001427559,0.00003052919,0.0002447787],"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":[0.00001392341,0.00004632868,0.0001598554,0.000111795,0.0001011829,0.00008538302,0.00001507662,0.00002311557,1.492919e-7,0.002496078,0.9969019,0.00004515368],"study_design_scores_gemma":[0.0003163443,0.00004630244,0.0001777896,0.000029518,0.00003825138,0.00000685947,0.000009773639,0.0005641419,0.000001158681,0.0003887178,0.9978651,0.00055607],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001073886,0.00001649682,0.00008365464,0.00004043729,0.001337595,0.0001784554,0.9954775,0.00004338944,0.002811726],"genre_scores_gemma":[0.00009804389,0.00273256,0.0001000261,0.001758745,0.0007596544,0.00001665933,0.9933074,0.00004030881,0.001186585],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.586821,"threshold_uncertainty_score":0.9997891,"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."}}