{"id":"W2018571487","doi":"10.1371/journal.pone.0102570","title":"Use of Physiologically-Based Pharmacokinetic Modeling to Simulate the Profiles of 3-Hydroxybenzo(a)pyrene in Workers Exposed to Polycyclic Aromatic Hydrocarbons","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Pesticide Exposure and Toxicity","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail","keywords":"Physiologically based pharmacokinetic modelling; Toxicokinetics; Pyrene; Inhalation exposure; Metabolite; Environmental chemistry; Chemistry; Inhalation; Urine; Pharmacokinetics; Xenobiotic; Exposure assessment; Benzo(a)pyrene; Toxicology; Pharmacology; Toxicity; Organic chemistry; Biology; Statistics; Biochemistry; Mathematics","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":[0.0002320507,0.0001576465,0.0003780651,0.00003461288,0.00005008162,0.00001601659,0.0003209871,0.00006320218,0.0000223173],"category_scores_gemma":[0.0003790468,0.00005901564,0.00007886865,0.000537218,0.00004473446,0.00004955833,0.00006712085,0.0001078046,0.00001064258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001265237,"about_ca_system_score_gemma":0.000007423661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003437,"about_ca_topic_score_gemma":0.0001777667,"domain_scores_codex":[0.99857,0.000168148,0.0004186807,0.0002596781,0.0003002738,0.0002832293],"domain_scores_gemma":[0.9989879,0.0006400798,0.0001144951,0.0001251868,0.00001535828,0.0001170195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001071627,0.0006474579,0.006579508,0.00001134864,0.00002584204,5.523447e-7,0.00006995672,0.01990332,0.970939,0.000004015257,0.000006284095,0.001705572],"study_design_scores_gemma":[0.0003120875,0.0009913507,0.07557479,0.0004627695,0.00007178729,1.283434e-7,0.00004100135,0.3155875,0.6059448,0.0007482421,0.00000883288,0.0002567167],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982558,0.00002079661,0.00008323517,0.001003032,0.00001394488,0.0005191058,0.00001082959,0.00003295783,0.00006029911],"genre_scores_gemma":[0.9981892,0.0000035035,0.001007633,0.0006688706,0.00005798936,0.00004269471,0.00001111881,0.000001998577,0.00001695817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3649942,"threshold_uncertainty_score":0.2406588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07983538809573931,"score_gpt":0.2389853305784179,"score_spread":0.1591499424826786,"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."}}