{"id":"W3035045131","doi":"10.18060/23898","title":"Going All in on AI","year":2020,"lang":"en","type":"article","venue":"Sports Innovation Journal","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Variety (cybernetics); Value proposition; Computer science; Active listening; The Internet; Competence (human resources); Marketing; Data science; Knowledge management; Telecommunications; World Wide Web; Business; Artificial intelligence; Sociology; Management; Economics","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":[0.002392838,0.0008064608,0.0005980697,0.001716399,0.003081209,0.0099867,0.001463943,0.003684128,0.04799713],"category_scores_gemma":[0.007018668,0.0002919607,0.0006788016,0.00134817,0.006193697,0.01116467,0.004013923,0.006881437,0.01964288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002339479,"about_ca_system_score_gemma":0.002887513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002382053,"about_ca_topic_score_gemma":0.002739992,"domain_scores_codex":[0.9978833,0.0007511986,0.00009451785,0.0004106667,0.0006188571,0.000241413],"domain_scores_gemma":[0.995887,0.001836491,0.0001369242,0.0006383003,0.0009451658,0.0005562108],"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":[0.00004171536,0.00007174246,0.0006250758,0.0004254268,0.00003068687,0.0001423494,0.001870153,0.0004713282,0.0007433017,0.5459014,0.2507513,0.1989256],"study_design_scores_gemma":[0.000003671883,0.00002260262,0.0001647759,0.0002978889,0.000005652109,0.0001032605,0.000664534,0.0002139804,0.0001828832,0.1223947,0.8759325,0.00001352637],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.003551267,0.06296413,0.04546025,0.2136153,0.0175327,0.00009479523,0.0003840767,0.0009399588,0.6554576],"genre_scores_gemma":[0.1437654,0.1566375,0.05658361,0.1123447,0.03117368,0.0002832775,0.001203052,0.001441658,0.496567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04799713,"threshold_uncertainty_score":0.1605664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09013657001479708,"score_gpt":0.2687010655208184,"score_spread":0.1785644955060213,"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."}}