{"id":"W4407930003","doi":"10.1016/j.chroma.2025.465816","title":"RT-Pred: A web server for accurate, customized liquid chromatography retention time prediction of chemicals","year":2025,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Chemistry; Retention time; Chromatography; High-performance liquid chromatography","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":[0.001716786,0.0002489416,0.000691018,0.00147087,0.0001034041,0.0001223585,0.001003141,0.000164072,0.00001967478],"category_scores_gemma":[0.0002600623,0.0002255011,0.00116602,0.002404888,0.0001205851,0.001183545,0.0001664295,0.0002201207,0.000002106468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005358144,"about_ca_system_score_gemma":0.0004124471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002622535,"about_ca_topic_score_gemma":4.173514e-7,"domain_scores_codex":[0.997096,0.0002937139,0.001362593,0.0003346884,0.000623076,0.0002899093],"domain_scores_gemma":[0.9967032,0.000686287,0.001200636,0.0004508161,0.0008357607,0.0001232899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004134886,0.001925163,0.003084193,0.001699227,0.003810156,0.00003166342,0.0008978493,0.008062223,0.8802714,0.03492428,0.05376187,0.007397057],"study_design_scores_gemma":[0.02614302,0.003575013,0.03582937,0.004284115,0.001140664,0.0005398692,0.0001627881,0.4204759,0.3931922,0.09876331,0.0146221,0.00127165],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6730365,0.0007049413,0.3239187,0.000473145,0.0007185538,0.0004638611,0.00004954941,0.00008865698,0.0005460518],"genre_scores_gemma":[0.9214715,0.0001032341,0.07804287,0.0001480203,0.0001304092,0.00003604505,0.00001388187,0.00001950091,0.00003451431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4870792,"threshold_uncertainty_score":0.9195666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01549868293411838,"score_gpt":0.2835753768468385,"score_spread":0.2680766939127201,"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."}}