{"id":"W2162424885","doi":"10.1177/1087057103258285","title":"Improved Statistical Methods for Hit Selection in High-Throughput Screening","year":2003,"lang":"en","type":"article","venue":"SLAS DISCOVERY","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":341,"is_retracted":false,"has_abstract":false,"ca_institutions":"Merck Canada Inc. (Canada)","funders":"","keywords":"Computer science; Automation; Throughput; Software; Variety (cybernetics); Selection (genetic algorithm); Process (computing); Data mining; Machine learning; Artificial intelligence; Operating system; Engineering","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":[0.02367831,0.001561785,0.004394013,0.004583113,0.001079775,0.002810269,0.004564515,0.002012873,0.002970802],"category_scores_gemma":[0.07879184,0.001180965,0.003425634,0.006014815,0.001831448,0.003262853,0.002803157,0.004722886,0.001388238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001604292,"about_ca_system_score_gemma":0.003689766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002291335,"about_ca_topic_score_gemma":0.002799289,"domain_scores_codex":[0.9752772,0.01729229,0.001007392,0.001443174,0.00451512,0.0004647637],"domain_scores_gemma":[0.9093806,0.07512575,0.00289306,0.008022198,0.003917587,0.0006608785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008798559,0.0004823832,0.005951518,0.001030215,0.001424428,0.0003936057,0.0002124695,0.4056169,0.01117524,0.1906472,0.0102983,0.3718879],"study_design_scores_gemma":[0.0000818738,0.0001058288,0.0006851301,0.00001865914,0.0001008537,0.00008496827,0.00001067602,0.9290224,0.001682174,0.06689527,0.001267893,0.00004426856],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003149076,0.0002788474,0.9951929,0.0001134288,0.00003405838,0.00004489314,0.0001190933,0.0009223064,0.0001454603],"genre_scores_gemma":[0.1615425,0.0009016996,0.8311275,0.0006007503,0.000366046,0.001134018,0.001584181,0.0007168166,0.002026431],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02367831,"threshold_uncertainty_score":0.1252244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065441171858109,"score_gpt":0.4750329075873824,"score_spread":0.3684887904015715,"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."}}