{"id":"W2905461632","doi":"10.1002/etc.4341","title":"Screening-level risk assessment of methylmercury for non-anadromous Arctic char (<i>Salvelinus alpinus</i>)","year":2018,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Environment and Climate Change Canada; Institut National de la Recherche Scientifique; University of Northern British Columbia; Alberta Environment and Protected Areas; McGill University","funders":"Northern Contaminants Program; Österreichischen Akademie der Wissenschaften","keywords":"Salvelinus; Arctic char; Fish migration; Methylmercury; Environmental science; Ecology; Char; Arctic; Zoology; Environmental chemistry; Biology; Fishery; Chemistry; Fish <Actinopterygii>; Habitat; Trout","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":[0.002015396,0.0006281099,0.0003507536,0.0009401468,0.0004208971,0.0005246791,0.0004259571,0.0003913546,0.0005199829],"category_scores_gemma":[0.001585376,0.0001913961,0.0006969104,0.0005465231,0.0002884277,0.0001886249,0.000465936,0.0001699769,0.0001196196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001041376,"about_ca_system_score_gemma":0.001416546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02856613,"about_ca_topic_score_gemma":0.06487795,"domain_scores_codex":[0.9990688,0.0003499048,0.00004788777,0.000126916,0.0003208945,0.00008573583],"domain_scores_gemma":[0.9987947,0.0002379016,0.0004582552,0.00004862761,0.0003655828,0.0000949635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001323511,0.0001952881,0.931614,0.0001014174,0.000313621,0.0004381493,0.0003054341,0.01257127,0.03619951,0.0001236023,0.0001731078,0.01664094],"study_design_scores_gemma":[0.00005399753,0.005080211,0.9517548,0.00003252859,0.0006355629,0.0004465841,0.0005744859,0.01859818,0.02172135,0.0003947881,0.0006788233,0.00002875587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975066,0.00007723165,0.001559953,0.0000169331,0.000001314619,0.00006003671,0.0001215889,0.00001740448,0.0006390866],"genre_scores_gemma":[0.99767,0.00006794834,0.001834755,0.00001376027,0.000001025755,0.00002113843,0.0001300242,0.000001658538,0.0002596752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02856613,"threshold_uncertainty_score":0.05679971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01773527642301823,"score_gpt":0.2800117013346273,"score_spread":0.2622764249116091,"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."}}