{"id":"W2896463595","doi":"10.1016/j.chemosphere.2018.10.041","title":"A framework for application of quantitative property-property relationships (QPPRs) in physiologically based pharmacokinetic (PBPK) models for high-throughput prediction of internal dose of inhaled organic chemicals","year":2018,"lang":"en","type":"article","venue":"Chemosphere","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail; Université de Montréal","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta","keywords":"Physiologically based pharmacokinetic modelling; Toxicokinetics; Pharmacokinetics; Chemistry; Inhalation exposure; Organic chemicals; Partition coefficient; Biological system; Environmental chemistry; Chromatography; Pharmacology; Organic chemistry; Toxicity; Medicine","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":[0.00294287,0.001105409,0.001146205,0.0008347052,0.0007155976,0.001513278,0.002277434,0.001460863,0.002553492],"category_scores_gemma":[0.005460401,0.0006652714,0.001894296,0.0008225353,0.001299203,0.001591897,0.00173488,0.002372316,0.0006767306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009852401,"about_ca_system_score_gemma":0.001994194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004000671,"about_ca_topic_score_gemma":0.003140912,"domain_scores_codex":[0.9991339,0.0004532279,0.00006210214,0.0001014583,0.0002098986,0.000039492],"domain_scores_gemma":[0.9981937,0.001315719,0.000115455,0.0001623515,0.0001660745,0.00004672624],"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":[0.00001472424,0.00007299402,0.0002114125,0.00008467729,0.00004940384,0.0001197153,0.00005414124,0.6677001,0.001690167,0.3144449,0.0009393079,0.01461856],"study_design_scores_gemma":[0.000004526551,0.00001302974,0.00002291088,0.000006313268,0.000008910551,0.00001780599,0.000004795142,0.9577664,0.0001936749,0.04081034,0.001146111,0.000005168651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001205045,0.00009891708,0.9974141,0.0001748934,0.00001706768,0.00002598037,0.00005039519,0.00009979962,0.0009137269],"genre_scores_gemma":[0.1757427,0.0008334816,0.819072,0.0002499762,0.0001255815,0.0005832476,0.0002459479,0.0001865753,0.002960458],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004000671,"threshold_uncertainty_score":0.01556361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08026671762196479,"score_gpt":0.3389318890601349,"score_spread":0.2586651714381701,"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."}}