{"id":"W2891902025","doi":"10.1515/jqas-2017-0122","title":"Modified Kelly criteria","year":2018,"lang":"en","type":"article","venue":"Journal of Quantitative Analysis in Sports","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Extension (predicate logic); Estimator; Mathematics; Computer science; Function (biology); Mathematical economics; Artificial intelligence; Statistics; Econometrics","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":[0.001752666,0.0001272088,0.0007708193,0.001552073,0.00005241112,0.00005098316,0.0002145385,0.00006308149,0.00192299],"category_scores_gemma":[0.0000982875,0.0001218095,0.000361668,0.001512268,0.0001064015,0.0003488689,0.00002246658,0.0001630996,0.00005245996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005881318,"about_ca_system_score_gemma":0.00002910077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001214448,"about_ca_topic_score_gemma":0.0001363934,"domain_scores_codex":[0.9980647,0.00001255799,0.001403367,0.0002066071,0.0001040876,0.0002086742],"domain_scores_gemma":[0.9980342,0.00003688236,0.001369937,0.0002260931,0.0002532305,0.0000795905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008419041,0.0001374267,0.9246082,0.000008074816,0.0004539657,0.00009691266,0.001292264,0.003508787,0.00001610874,0.06868494,0.000869331,0.000239794],"study_design_scores_gemma":[0.0004601488,0.0002857016,0.888513,0.00003375594,0.0001757855,0.0000103552,0.0003327762,0.07651107,0.00003751468,0.02243014,0.01095413,0.0002556411],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729173,0.0009260703,0.01358622,0.0001766304,0.0003791398,0.00003644063,0.00001519871,0.000003309591,0.01195973],"genre_scores_gemma":[0.9966466,0.0004193264,0.002145343,0.0001736674,0.0001649633,6.989245e-7,0.000003778233,0.00001081045,0.0004348422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07300229,"threshold_uncertainty_score":0.9989894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0725672907526086,"score_gpt":0.3216534555276852,"score_spread":0.2490861647750766,"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."}}