{"id":"W2301505581","doi":"10.1515/jqas-2014-0093","title":"Riding a probabilistic support vector machine to the Stanley Cup","year":2015,"lang":"en","type":"article","venue":"Journal of Quantitative Analysis in Sports","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Context (archaeology); League; Machine learning; Artificial intelligence; Computer science; Relevance vector machine; Probabilistic logic; History","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003798135,0.0008679636,0.0006162035,0.001806118,0.0003987151,0.001544746,0.0008996156,0.0006890016,0.003270844],"category_scores_gemma":[0.01396666,0.0001750909,0.000575653,0.001421641,0.0004080927,0.001025591,0.0009573131,0.001140491,0.0009365162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009461047,"about_ca_system_score_gemma":0.0008135143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01461678,"about_ca_topic_score_gemma":0.00730956,"domain_scores_codex":[0.9986307,0.000621428,0.00006456861,0.0003241542,0.0002301816,0.0001289424],"domain_scores_gemma":[0.9947542,0.003315399,0.0005695656,0.0002767254,0.0008507061,0.0002334519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001205238,0.0005290526,0.2718949,0.0001650154,0.0002533105,0.0002821626,0.0001662763,0.5770543,0.0006813115,0.004027158,0.008720445,0.1350209],"study_design_scores_gemma":[0.00001045107,0.0001543677,0.02726986,0.0000218338,0.00001420186,0.00002778206,0.0001410617,0.9698699,0.00030804,0.001684697,0.0004853029,0.00001248376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658778,0.0002671555,0.02915212,0.0003831385,0.0001207668,0.00006789611,0.0007461685,0.0002850521,0.00309992],"genre_scores_gemma":[0.9942215,0.0000467303,0.003228247,0.00002080306,0.00003078594,0.00002136904,0.00151555,0.00001290567,0.0009020434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01461678,"threshold_uncertainty_score":0.0290634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08794562066108803,"score_gpt":0.3064188621080692,"score_spread":0.2184732414469812,"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."}}