{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005762469,0.00008411992,0.00018523,0.0003908954,0.000051435,0.00007651625,0.00009991636,0.00004643471,0.001503869],"category_scores_gemma":[0.00005872421,0.00009079389,0.00003627333,0.0007614184,0.00001162378,0.0002391306,0.00001225816,0.0003649455,0.0001559749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005171579,"about_ca_system_score_gemma":0.00002546532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003804837,"about_ca_topic_score_gemma":7.970849e-7,"domain_scores_codex":[0.9988279,0.000001870416,0.0007806908,0.0001617213,0.00005874576,0.0001690734],"domain_scores_gemma":[0.9993816,0.000004312602,0.0004203243,0.00008819953,0.00005223822,0.00005330833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000258543,0.00004117575,0.5904202,0.000008649195,0.00001328272,0.00007329476,0.0003908543,0.003870422,0.00001150635,0.3927727,0.008693055,0.003678898],"study_design_scores_gemma":[0.0007072335,0.0001136652,0.2810031,0.00004116919,0.000001941862,0.00002802475,0.00004834487,0.02455967,0.00005046383,0.01772299,0.6754546,0.0002687442],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9605394,0.0001389537,0.003059211,0.01172341,0.0005611336,0.00006607782,0.000008232452,0.00001609074,0.02388746],"genre_scores_gemma":[0.9860194,0.0001165268,0.0001561966,0.01306143,0.0003503166,0.000001071105,0.000009087388,0.00001217454,0.0002737441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6667616,"threshold_uncertainty_score":0.9994089,"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."}}