{"id":"W7126433731","doi":"10.21428/594757db.ec493180","title":"Advancing NHL Analytics through Explainable AI","year":2024,"lang":"en","type":"article","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Key (lock); Bridge (graph theory); Analytics; League; Reliability (semiconductor); Applications of artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0152069,0.001251108,0.0004835586,0.003932513,0.001679353,0.007151945,0.003671857,0.002359758,0.004360061],"category_scores_gemma":[0.04406583,0.0006383857,0.001438107,0.001498524,0.006958917,0.01283673,0.009965771,0.004419484,0.0006238534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003393641,"about_ca_system_score_gemma":0.00607259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006934217,"about_ca_topic_score_gemma":0.006184447,"domain_scores_codex":[0.9878142,0.008126653,0.000525191,0.00112806,0.001998262,0.0004077226],"domain_scores_gemma":[0.9574379,0.02967614,0.002613126,0.007310239,0.002342135,0.0006204915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005055616,0.0001788903,0.004556573,0.0003591845,0.00008591318,0.0002818253,0.009234195,0.02447066,0.001467769,0.8768759,0.001924795,0.08051383],"study_design_scores_gemma":[0.00003585672,0.0000944089,0.001145581,0.0005853162,0.00006440511,0.0001851945,0.003149405,0.1189248,0.002407036,0.8154516,0.05789791,0.00005856741],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01251485,0.0003801402,0.9605916,0.007908966,0.000061638,0.0002579669,0.0001628166,0.0009306975,0.01719134],"genre_scores_gemma":[0.4010302,0.0005817061,0.5934274,0.0007803285,0.00008611397,0.0004271348,0.0006599546,0.0002047351,0.002802443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0152069,"threshold_uncertainty_score":0.08042276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02519146709917447,"score_gpt":0.3129547127266191,"score_spread":0.2877632456274446,"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."}}