{"id":"W2166393666","doi":"10.1093/bioinformatics/btm145","title":"An efficient method for the detection and elimination of systematic error in high-throughput screening","year":2007,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University and Génome Québec Innovation Centre; Université du Québec à Montréal","funders":"","keywords":"Throughput; Computer science; Error detection and correction; Systematic error; High-throughput screening; Statistics; Algorithm; Mathematics; Biology; Bioinformatics; Operating system","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":[0.00446813,0.00008629349,0.0001710914,0.0001921271,0.00008857468,0.00007259738,0.0002960112,0.00004142279,2.166689e-7],"category_scores_gemma":[0.0003269601,0.00006243267,0.00003458738,0.0003848044,0.00002630972,0.0004071337,0.00007384294,0.00005749558,5.429577e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004321508,"about_ca_system_score_gemma":0.00002552719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003089742,"about_ca_topic_score_gemma":0.00002903421,"domain_scores_codex":[0.9988083,0.000102887,0.0005669396,0.0001048073,0.000268052,0.000149072],"domain_scores_gemma":[0.9972931,0.001993656,0.0002856565,0.0002766524,0.0001188688,0.00003202301],"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":[0.00003889209,0.00008065776,0.00005282395,0.005226727,0.00002337209,5.889434e-7,0.0103093,0.6408103,0.0004026433,0.07553052,0.000002811959,0.2675213],"study_design_scores_gemma":[0.0002695879,0.00008436944,0.009112533,0.0002162822,0.00001409935,0.000009227282,0.0008599348,0.9866844,0.001825005,0.0008470303,0.000004722277,0.00007281084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02157349,0.00006737121,0.9773583,0.00008186477,0.0001681243,0.0006958808,0.000002509818,0.00002772213,0.00002470628],"genre_scores_gemma":[0.4515446,0.000001068639,0.5483957,0.00003118289,0.000009963919,0.00001267022,0.000001033752,0.000002623881,0.000001167059],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4299711,"threshold_uncertainty_score":0.254593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03858466843007075,"score_gpt":0.346586647669036,"score_spread":0.3080019792389652,"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."}}