{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02911002,0.0013322,0.002673616,0.001608742,0.0005171684,0.001574632,0.002734544,0.00169151,0.005862962],"category_scores_gemma":[0.09817887,0.0008513648,0.001133023,0.001493141,0.003581876,0.002457362,0.002523921,0.001855613,0.0007060522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001984487,"about_ca_system_score_gemma":0.002753127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001593942,"about_ca_topic_score_gemma":0.0009541265,"domain_scores_codex":[0.971068,0.02444647,0.0005181691,0.001587206,0.001824817,0.0005552687],"domain_scores_gemma":[0.9534027,0.03993395,0.002776095,0.001873601,0.001586308,0.0004273952],"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":[0.0007247279,0.0002424403,0.001519756,0.0002133912,0.0002580211,0.0001700179,0.0001970325,0.6068093,0.001175754,0.2798638,0.001357481,0.1074682],"study_design_scores_gemma":[0.0001657556,0.000143434,0.0003371528,0.00002682537,0.00002780656,0.00005033975,0.00001980878,0.8867072,0.000733173,0.1111879,0.0005791203,0.00002135354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009507146,0.0001174535,0.9890881,0.0002513666,0.00001698299,0.0001498663,0.0000182891,0.0001022564,0.0007486765],"genre_scores_gemma":[0.4242173,0.000215816,0.571871,0.0002679021,0.00008884975,0.001249252,0.00009286011,0.0001121738,0.001884744],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02911002,"threshold_uncertainty_score":0.1539504,"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."}}