{"id":"W4398168374","doi":"10.1101/2024.05.16.594622","title":"Enhanced Thompson Sampling by Roulette Wheel Selection for Screening Ultra-Large Combinatorial Libraries","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"AstraZeneca (Canada)","funders":"","keywords":"Roulette; Selection (genetic algorithm); Sampling (signal processing); Fitness proportionate selection; Computer science; Artificial intelligence; Statistics; Machine learning; Mathematics; Computer vision; Genetic algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002227204,0.000668906,0.001526943,0.001302666,0.0004701795,0.0008309167,0.001870624,0.0009598177,0.001669341],"category_scores_gemma":[0.006153986,0.0003511076,0.0006152891,0.001519667,0.0006699181,0.0009286352,0.0009944589,0.0008458674,0.0006093761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008971551,"about_ca_system_score_gemma":0.001437252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004369813,"about_ca_topic_score_gemma":0.005593568,"domain_scores_codex":[0.9982174,0.0008490533,0.00007869581,0.0001892622,0.0005108623,0.000154771],"domain_scores_gemma":[0.9971738,0.001684016,0.0001647319,0.0004772888,0.0003833399,0.0001167104],"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":[0.0008219442,0.0002986109,0.002973808,0.0001253614,0.0001126687,0.0002034315,0.0001082346,0.7120581,0.01796665,0.01282297,0.004752593,0.2477556],"study_design_scores_gemma":[0.00003237853,0.00003556422,0.00018333,0.000002727543,0.000006576185,0.00001759754,0.000004102208,0.9947959,0.002415774,0.002137767,0.0003615686,0.000006808796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1016451,0.0004693887,0.8913702,0.0003227528,0.00007469497,0.0001965463,0.0002029936,0.003641138,0.002077243],"genre_scores_gemma":[0.5836887,0.0001590216,0.4124953,0.0002677884,0.00005935218,0.0005078969,0.0006200295,0.0002344311,0.001967502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004369813,"threshold_uncertainty_score":0.01177871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602251742608944,"score_gpt":0.2384339786619829,"score_spread":0.2224114612358934,"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."}}