{"id":"W2963874364","doi":"10.1017/s0269964818000256","title":"OPTIMAL SELECTION OF THE <i>k</i>-TH BEST CANDIDATE","year":2019,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Optimal stopping; Combinatorics; Mathematics; Selection (genetic algorithm); Stopping rule; Discrete mathematics; Statistics; Mathematical optimization; Computer science; Artificial intelligence","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":[0.001044695,0.0000413409,0.00004545929,0.00004390244,0.00008548547,0.00008755813,0.0004511205,0.000014825,0.000006573332],"category_scores_gemma":[0.00006447057,0.00002212293,0.00001504414,0.0005337313,0.00006775097,0.0006493991,0.0000800165,0.00007597411,0.000003357149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001397531,"about_ca_system_score_gemma":0.00005869025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000274848,"about_ca_topic_score_gemma":0.000004382546,"domain_scores_codex":[0.999386,0.00002607167,0.0001480853,0.0000742841,0.0002629884,0.0001025699],"domain_scores_gemma":[0.9997289,0.00008213575,0.00003911079,0.00009880667,0.00003722139,0.00001382681],"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":[5.217039e-7,0.00000916443,0.00300107,0.00002074294,7.717709e-7,4.933342e-9,0.001040779,0.9456163,0.00004078005,0.04986313,0.00000921753,0.0003975321],"study_design_scores_gemma":[0.0000593371,0.00003608214,0.004861298,0.00001353373,3.464048e-7,0.000004244026,0.00004686071,0.9936638,0.0001463519,0.0007310556,0.0004028043,0.00003425192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882259,0.00002007462,0.007741693,0.001403423,0.0001159306,0.0002997231,0.000001435122,0.00001945068,0.002172316],"genre_scores_gemma":[0.9945011,0.000006610243,0.005389288,0.00007144359,0.000005000951,0.000008548536,3.915323e-7,5.44363e-7,0.00001703774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04913208,"threshold_uncertainty_score":0.09021467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01061213047116742,"score_gpt":0.2182559173910074,"score_spread":0.20764378691984,"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."}}