{"id":"W4398293794","doi":"10.7910/dvn/ii5jzg/58hwrx","title":"MSP_F_20_NFL_4_23.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":"Context (archaeology); Resolution (logic); Computer science; History; Artificial intelligence; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001228927,0.00250242,0.001542422,0.004684272,0.0008610184,0.004187868,0.003178828,0.002580554,0.3983476],"category_scores_gemma":[0.0103813,0.0009877834,0.00129675,0.008471237,0.0006132962,0.002641966,0.003260224,0.001796306,0.3795572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001881436,"about_ca_system_score_gemma":0.002380026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02813082,"about_ca_topic_score_gemma":0.0382241,"domain_scores_codex":[0.9986051,0.0002008101,0.0001680355,0.0003265434,0.0003245034,0.0003749591],"domain_scores_gemma":[0.9962797,0.0009471183,0.0003832973,0.0007713588,0.001136753,0.0004819211],"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":[0.00002489618,0.000009661968,0.0003371327,0.0002698894,0.00000976411,0.000005768044,0.00001095069,0.00006893816,0.00002741086,0.0002735073,0.9981416,0.0008204817],"study_design_scores_gemma":[0.0003125069,0.00001911186,0.003475201,0.0003372653,0.00001563481,0.00002941623,0.00009664123,0.0002288935,0.0002179805,0.001211884,0.994023,0.00003238058],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003629396,0.00001867131,0.00001895912,0.00005297482,0.00001800526,0.000004637509,0.9990181,0.0002158655,0.0006165848],"genre_scores_gemma":[0.0002976463,0.0000388367,0.0001371846,0.00007327434,0.00001617544,0.00005880257,0.9978626,0.0001602504,0.001355218],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6016524,"threshold_uncertainty_score":0.8581841,"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."}}