{"id":"W4409059348","doi":"10.1021/acs.est.4c11870","title":"Transcriptomics Points-of-Departure (tPODs) to Support Hazard Assessment of Benzo[<i>a</i>]pyrene in Early-Life-Stage Rainbow Trout","year":2025,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Environment and Climate Change Canada; University of Saskatchewan; Global Institute for Water Security; University of Ottawa","funders":"Government of Canada; Ministère de l'Économie, de la Science et de l'Innovation - Québec; University of Saskatchewan; Genome Prairie; Génome Québec; Shell; McGill University; Environment and Climate Change Canada; Innovation Saskatchewan; Genome Canada; QIAGEN","keywords":"Rainbow trout; Benzo(a)pyrene; Stage (stratigraphy); Trout; Environmental chemistry; Pyrene; Fish <Actinopterygii>; Hazard; Environmental science; Chemistry; Fishery; Biology; Ecology; Organic 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":[0.0008471322,0.0005227433,0.0003061873,0.0004153956,0.0002489221,0.0005425903,0.000355789,0.0005082553,0.001005001],"category_scores_gemma":[0.0007299213,0.0002320083,0.0004095958,0.0003545915,0.0003462835,0.000437757,0.0006640162,0.0007784583,0.0003443319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006218896,"about_ca_system_score_gemma":0.0005892802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002045315,"about_ca_topic_score_gemma":0.006102775,"domain_scores_codex":[0.9995421,0.00005807868,0.00002475205,0.0001429505,0.0002041017,0.00002801124],"domain_scores_gemma":[0.9995472,0.00009311082,0.0001168591,0.00004700388,0.0001603589,0.00003540751],"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":[0.0000992212,0.00003250256,0.006651227,0.00007166543,0.00001507654,0.00002966499,0.00005917162,0.0009007266,0.9855062,0.0001736903,0.0001188083,0.006342002],"study_design_scores_gemma":[0.00002464733,0.001137854,0.08953312,0.00003480846,0.00008379103,0.0001783301,0.0003281871,0.01225935,0.8884424,0.0009094386,0.007009899,0.00005814168],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8288125,0.0009660968,0.1562337,0.0003761559,0.0001362358,0.0005227539,0.007946638,0.0007348548,0.004271009],"genre_scores_gemma":[0.8469929,0.001172066,0.1402168,0.0003760332,0.0000395005,0.0008125735,0.005881973,0.0001103604,0.004397783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002045315,"threshold_uncertainty_score":0.004512131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006854653827643518,"score_gpt":0.2652375187423481,"score_spread":0.2583828649147046,"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."}}