{"id":"W4383482171","doi":"10.1002/etc.5711","title":"The Comet Assay, a Sensitive Biomarker of Water Quality Improvement Following Adoption of Beneficial Agricultural Practices?","year":2023,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Environmental Toxicology and Ecotoxicology","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs; Ministère des Ressources naturelles et des Forêts; Environment and Climate Change Canada","funders":"Agriculture and Agri-Food Canada; Environment and Climate Change Canada","keywords":"Comet assay; Genotoxicity; Mussel; Metolachlor; Tributary; Toxicology; Freshwater ecosystem; DNA damage; Pesticide; Environmental science; Biology; Environmental chemistry; Fishery; Ecology; Chemistry; Ecosystem; Atrazine; Toxicity; DNA; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002220508,0.0008242819,0.000921969,0.001576785,0.0003178059,0.001515687,0.0006591791,0.002549973,0.001776004],"category_scores_gemma":[0.003485643,0.0003981396,0.0004548604,0.001065134,0.001384656,0.001564845,0.0004667831,0.001028178,0.00125537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006329549,"about_ca_system_score_gemma":0.0005051955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001253781,"about_ca_topic_score_gemma":0.003003029,"domain_scores_codex":[0.9973332,0.0006470053,0.0002042553,0.0006616415,0.0009639385,0.0001899469],"domain_scores_gemma":[0.9969747,0.0004495159,0.001219451,0.000189502,0.0009728743,0.0001939238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004790855,0.0002807973,0.09965464,0.002114927,0.0004179335,0.0007178282,0.0007160184,0.0002773548,0.8027218,0.00152593,0.005869523,0.0852242],"study_design_scores_gemma":[0.00008452125,0.005184838,0.2004107,0.001255161,0.0005303077,0.006412597,0.002797851,0.002234826,0.6570156,0.006160228,0.1176455,0.0002678408],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6817566,0.1658803,0.08283737,0.03622721,0.00436966,0.0005490036,0.004461395,0.001509592,0.02240887],"genre_scores_gemma":[0.9219788,0.02899136,0.02104579,0.007288333,0.0008940074,0.0002553343,0.001591371,0.00007171392,0.01788326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002549973,"threshold_uncertainty_score":0.01174331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01459968092408402,"score_gpt":0.2594137475956003,"score_spread":0.2448140666715163,"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."}}