{"id":"W4415828576","doi":"10.1016/j.ijheh.2025.114703","title":"Determinants of human hair mercury, blood mercury, blood selenium, and plasma omega-3 fatty acid levels within northern Canada","year":2025,"lang":"en","type":"article","venue":"International Journal of Hygiene and Environmental Health","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Yukon Health and Social Services; Yukon University; Université de Montréal; University of Waterloo; Health Canada","funders":"Dehcho First Nations; Global Water Futures; Northern Contaminants Program; Natural Sciences and Engineering Research Council of Canada; Université de Montréal; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Health Canada; Canada Research Chairs","keywords":"Biomonitoring; Biomarker; Docosahexaenoic acid; Eicosapentaenoic acid; Mercury (programming language); MERCURY EXPOSURE; Whole blood; Waterfowl","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":[0.0005389318,0.0002990841,0.0003435808,0.0007563315,0.00187107,0.001042097,0.0006538451,0.0002838287,0.001737597],"category_scores_gemma":[0.001463378,0.0002990439,0.0004264359,0.00253001,0.0006289839,0.000225566,0.0005899874,0.000379278,0.0001541479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01142803,"about_ca_system_score_gemma":0.01914313,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9931381,"about_ca_topic_score_gemma":0.9953532,"domain_scores_codex":[0.9993417,0.00007278904,0.00002743504,0.0001586064,0.0001831397,0.0002163749],"domain_scores_gemma":[0.9987926,0.0001025781,0.0001797132,0.00005894264,0.0005404613,0.0003257502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000765265,0.00001941284,0.9921389,0.00002695867,0.00007752234,0.0001172292,0.0008493495,0.0002776849,0.000654458,0.0001894793,0.0006063113,0.004966286],"study_design_scores_gemma":[0.000002946048,0.00001048885,0.9975584,0.00001878486,0.00002380752,0.00006037464,0.001010059,0.0004131311,0.0001038272,0.00004616629,0.0007451381,0.000006892084],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945339,0.0007277743,0.0003963359,0.0002723616,0.000006605708,0.00002981573,0.001636835,0.00002273801,0.002373667],"genre_scores_gemma":[0.9977313,0.0003208627,0.0003943598,0.00005323537,0.000002139703,0.00001043786,0.0003830883,0.000006053146,0.00109848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01142803,"threshold_uncertainty_score":0.08291656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338294761393882,"score_gpt":0.2729324294138363,"score_spread":0.2595494817998975,"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."}}