{"id":"W2009482316","doi":"10.1080/07474940600596695","title":"Sequential Generalized Likelihood Ratios and Adaptive Treatment Allocation for Optimal Sequential Selection","year":2006,"lang":"en","type":"article","venue":"Sequential Analysis","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National University of Singapore; University of Lethbridge; National Science Foundation","keywords":"Mathematics; Selection (genetic algorithm); Mathematical optimization; Sequential estimation; Sampling (signal processing); Exponential family; Constraint (computer-aided design); Population; Sequential analysis; Stopping rule; Statistics; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00140066,0.0004062233,0.0007762409,0.001306688,0.0007075773,0.0006773046,0.0003886909,0.0002038528,0.0006640067],"category_scores_gemma":[0.0002321421,0.0003303762,0.0007279811,0.002865991,0.0002369557,0.0007942565,0.0001203413,0.0001454977,0.00006147219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005708117,"about_ca_system_score_gemma":0.0003028348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003299803,"about_ca_topic_score_gemma":0.005885683,"domain_scores_codex":[0.9947983,0.000497861,0.001074259,0.001299496,0.00160087,0.0007291982],"domain_scores_gemma":[0.9973224,0.0003573932,0.0004590711,0.0004929973,0.001145746,0.0002224075],"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":[0.002421742,0.0009836793,0.004041426,0.00001897067,0.006498287,0.00005093318,0.001004155,0.7641624,0.1065519,0.005650093,0.0029713,0.1056452],"study_design_scores_gemma":[0.003551661,0.000850947,0.00211558,0.000005547841,0.002502112,0.0000212353,0.0003655582,0.908379,0.05761039,0.01903205,0.004896256,0.0006696888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2356958,0.0001813841,0.7623686,0.0003777479,0.0002144115,0.0007870815,0.0001718633,0.00008427311,0.0001188412],"genre_scores_gemma":[0.9615157,0.00004732666,0.03239565,0.00003608788,0.001029274,0.0002999853,0.0005753352,0.00003748128,0.004063217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7299729,"threshold_uncertainty_score":0.9999148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08329802728424093,"score_gpt":0.3970339346573362,"score_spread":0.3137359073730952,"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."}}