{"id":"W65262223","doi":"10.1007/3-540-34416-0_26","title":"Comparison of Two Methods for Detecting and Correcting Systematic Error in High-throughput Screening Data","year":2006,"lang":"en","type":"book-chapter","venue":"Studies in classification, data analysis, and knowledge organization","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Throughput; Selection (genetic algorithm); Systematic error; High-throughput screening; Process (computing); Drug discovery; Data mining; Machine learning; Bioinformatics; Statistics; Mathematics; Biology","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":[0.02218618,0.001868687,0.00253046,0.006449954,0.001198729,0.00349291,0.003806859,0.003053746,0.002385609],"category_scores_gemma":[0.05894082,0.0008716377,0.002114169,0.005124383,0.001323605,0.003512937,0.002677325,0.001826244,0.0008649292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001950635,"about_ca_system_score_gemma":0.00259459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004649905,"about_ca_topic_score_gemma":0.007893945,"domain_scores_codex":[0.9855885,0.005561823,0.00126339,0.001602789,0.005500393,0.0004831751],"domain_scores_gemma":[0.8724609,0.1055573,0.003055615,0.008304597,0.009914832,0.0007067178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005229537,0.0009920461,0.02287417,0.001744618,0.002570534,0.0001888711,0.0006735431,0.02881106,0.02641936,0.008585643,0.007257046,0.8946536],"study_design_scores_gemma":[0.001666662,0.001350966,0.05187487,0.0002864409,0.001724489,0.001318114,0.0005481158,0.8273915,0.08668616,0.01836991,0.008376834,0.0004059923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1008155,0.006194309,0.8808463,0.001041869,0.0005413531,0.0005079114,0.001540052,0.006602772,0.001910041],"genre_scores_gemma":[0.1522212,0.001605177,0.8391982,0.0003531163,0.000181471,0.0006183013,0.002728252,0.0006786635,0.00241562],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02218618,"threshold_uncertainty_score":0.1173331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2944644511487388,"score_gpt":0.5011863299425744,"score_spread":0.2067218787938356,"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."}}