{"id":"W2287096358","doi":"10.1080/00949655.2015.1136629","title":"Optimal lineups in Twenty20 cricket","year":2016,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cricket; Mathematics; Selection (genetic algorithm); Simulated annealing; Operations research; Statistics; Artificial intelligence; Mathematical optimization; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0003470608,0.00005117359,0.0001752044,0.0001791837,0.00002294388,0.00002908408,0.00002954757,0.00003028491,0.0001613213],"category_scores_gemma":[0.0001316242,0.00003961676,0.0000229706,0.00008018691,0.00002534932,0.0001892251,0.000007839866,0.00005754273,0.00001507633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003203307,"about_ca_system_score_gemma":0.00000961023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006705493,"about_ca_topic_score_gemma":0.000002101389,"domain_scores_codex":[0.9992185,0.000007839611,0.0005696342,0.00008251591,0.00004211355,0.00007941947],"domain_scores_gemma":[0.9993657,0.0001977206,0.0002917242,0.00002859783,0.00006294915,0.00005327581],"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":[0.0001015367,0.00008242577,0.1428737,0.0000193569,0.00001789031,0.00001970095,0.0002716412,0.7367057,0.0000101262,0.09148652,0.0002630714,0.02814836],"study_design_scores_gemma":[0.0006871251,0.00009344373,0.273207,0.00001928616,0.000002619569,0.000004262965,0.00001183924,0.7032125,0.000001461925,0.02099525,0.001706069,0.00005915084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3863727,0.00008930505,0.6129492,0.0002718287,0.00009310505,0.00002349483,0.00001702752,0.000001799587,0.0001815387],"genre_scores_gemma":[0.9936897,0.00007345629,0.006048386,0.00006800509,0.00006600036,2.302994e-7,0.000002798775,0.00000453233,0.00004687662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.607317,"threshold_uncertainty_score":0.1766355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03511956244643052,"score_gpt":0.2843340527956573,"score_spread":0.2492144903492268,"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."}}