{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001167009,0.0002022619,0.0004126723,0.00007329929,0.0000944134,0.0000581962,0.00008172191,0.0001116195,0.0000524636],"category_scores_gemma":[0.005889406,0.0001834062,0.00007158615,0.0001804251,0.00006315094,0.0003237475,0.00002814487,0.0002133502,0.000001495721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009319883,"about_ca_system_score_gemma":0.0000683987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003948294,"about_ca_topic_score_gemma":0.00005194084,"domain_scores_codex":[0.9981362,0.0004294637,0.0004364048,0.0004261994,0.0001175385,0.000454233],"domain_scores_gemma":[0.9952627,0.004326604,0.0001090484,0.0001619206,0.0000587402,0.00008091568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008458589,0.00009727178,0.00004618064,0.0000824741,0.00002412417,0.00000174195,0.00006028559,0.0000840715,0.002771239,0.9576606,0.0001782304,0.03890917],"study_design_scores_gemma":[0.0009779523,0.0001650332,0.0001440326,0.00003109454,0.00004616687,0.000004524972,0.00008089132,0.02085077,0.004938051,0.9710705,0.001447375,0.0002435853],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00189011,0.00004627649,0.9965495,0.00003915972,0.0001994388,0.000534259,0.0001020935,0.00005202402,0.0005870822],"genre_scores_gemma":[0.03367163,0.000007831549,0.9651946,0.00008026113,0.00005865949,0.0001525823,0.00001231532,0.00004908093,0.0007730331],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03866559,"threshold_uncertainty_score":0.7479088,"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."}}