{"id":"W4382516992","doi":"10.1021/acs.analchem.3c00921","title":"Bridging the Gap between Differential Mobility, Log <i>S</i>, and Log <i>P</i> Using Machine Learning and SHAP Analysis","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Hong Kong Government; Government of Ontario; Ontario Ministry of Research, Innovation and Science; Government of Canada; Deutsche Forschungsgemeinschaft; Ontario Centres of Excellence","keywords":"Chemistry; Solubility; Partition coefficient; Analytical Chemistry (journal); Mean squared error; Aqueous solution; Statistics; Chromatography; Mathematics; Physical chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002705246,0.001061977,0.00067136,0.001428161,0.0002849284,0.00111069,0.000716998,0.0007607344,0.0009144189],"category_scores_gemma":[0.006520025,0.000282877,0.001033092,0.0006488771,0.0007698925,0.001544389,0.001192752,0.001289195,0.0001604228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007298308,"about_ca_system_score_gemma":0.0007935585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001891092,"about_ca_topic_score_gemma":0.001617441,"domain_scores_codex":[0.9992834,0.0002598497,0.00005112734,0.0001644478,0.0001931837,0.00004802988],"domain_scores_gemma":[0.995797,0.002939958,0.000507508,0.0003853299,0.0002893981,0.00008078487],"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":[0.0004212683,0.0002995754,0.03804667,0.0003725307,0.0005876372,0.0005979786,0.0004854886,0.7165121,0.04113696,0.03429532,0.001416536,0.1658279],"study_design_scores_gemma":[0.000004768343,0.00005828986,0.003387476,0.00001208067,0.00002836087,0.000038089,0.00003080402,0.9786997,0.004419378,0.01281372,0.0004868703,0.00002037911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5161576,0.0007018867,0.4790726,0.0006475875,0.0000384814,0.00003865398,0.0002757566,0.0009189395,0.002148457],"genre_scores_gemma":[0.9561782,0.0002149885,0.04268257,0.00008159739,0.0000375302,0.00002757012,0.0003684132,0.00006621187,0.0003429012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002705246,"threshold_uncertainty_score":0.0143069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02530241784934581,"score_gpt":0.2755753976153228,"score_spread":0.2502729797659771,"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."}}