{"id":"W2409013968","doi":"10.1016/j.envint.2016.05.025","title":"Dermal permeation data and models for the prioritization and screening-level exposure assessment of organic chemicals","year":2016,"lang":"en","type":"review","venue":"Environment International","topic":"Contact Dermatitis and Allergies","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; University of Toronto; The Scarborough Hospital; ARC Resources (Canada)","funders":"U.S. Environmental Protection Agency","keywords":"Organic chemicals; Prioritization; Partition coefficient; Percentile; Molecular descriptor; Environmental science; Chemistry; Statistics; Environmental chemistry; Quantitative structure–activity relationship; Computer science; Mathematics; Chromatography; Machine learning","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":[0.0001637414,0.00014932,0.0003695675,0.00004743422,0.00004405256,0.00002494828,0.0001699466,0.00008463991,0.0001961493],"category_scores_gemma":[0.0000385044,0.00009178065,0.00005831206,0.0000142492,0.00005688887,0.0001475177,0.0002362977,0.00008204696,9.429947e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005646261,"about_ca_system_score_gemma":0.0000485951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001153093,"about_ca_topic_score_gemma":7.727972e-7,"domain_scores_codex":[0.999023,0.00001985924,0.0003563458,0.0002527034,0.000270555,0.00007760148],"domain_scores_gemma":[0.9992082,0.0002337589,0.0002307289,0.0002719093,0.00002245459,0.0000329186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006941696,0.00004500663,0.0003263341,0.001145782,0.0004106826,0.000001031185,0.00003788905,0.000002720652,0.0001054694,0.0005455604,0.0003692682,0.9970033],"study_design_scores_gemma":[0.000954601,0.00005745186,0.003747339,0.004235344,0.0005834718,0.00007193699,0.00002836465,0.01032521,0.00001054693,0.00010061,0.9797183,0.0001668472],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001107678,0.8535107,0.1434159,0.0007850897,0.00008665828,0.0008898244,0.001074009,0.00000761874,0.0001194352],"genre_scores_gemma":[0.003963887,0.9903978,0.003119033,0.00004222833,0.0001848971,0.00006747402,0.001755763,0.00002142023,0.0004475251],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9968365,"threshold_uncertainty_score":0.3742706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09622464508392235,"score_gpt":0.3518406203548691,"score_spread":0.2556159752709468,"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."}}