{"id":"W7010352948","doi":"","title":"Human Biomonitoring of and Determinants of Biomarker Levels for Contaminants and Nutrients in Old Crow, Yukon Territory","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Indigenous Studies and Ecology","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Global Water Futures","keywords":"Biomonitoring; Hexachlorobenzene; Nutrient; Contamination; Population; Pollutant; Biomarker; Human health","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004132602,0.0001939177,0.0001800268,0.0005339147,0.001539179,0.0007528659,0.0005047009,0.000358028,0.001021499],"category_scores_gemma":[0.0008180646,0.0001732317,0.0001869035,0.001170047,0.0005393023,0.0001997274,0.0005789793,0.000230587,0.0001772687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004064152,"about_ca_system_score_gemma":0.003996435,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7725456,"about_ca_topic_score_gemma":0.8995112,"domain_scores_codex":[0.999541,0.00007883165,0.00003463878,0.0001181306,0.0001165345,0.0001108168],"domain_scores_gemma":[0.9994373,0.00005353761,0.00009249436,0.00003398598,0.0003211099,0.00006162646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006223219,0.00004444816,0.9816519,0.00006971523,0.00004947545,0.0002936207,0.003631374,0.000172359,0.002338687,0.0001509774,0.0008012382,0.01073383],"study_design_scores_gemma":[0.000001612397,0.0000445606,0.991886,0.00002234745,0.00001703383,0.00008393567,0.006186324,0.00009737164,0.000272902,0.00002955239,0.001352244,0.000006087294],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973406,0.0001531881,0.0002092645,0.000100291,0.000005306671,0.00002576456,0.0007212621,0.000004454187,0.001439751],"genre_scores_gemma":[0.9970687,0.0001777096,0.0004036828,0.0000772658,0.000001441908,0.00002106877,0.0004971154,0.000002214558,0.001750862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2274544,"threshold_uncertainty_score":0.4575877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05223278171230835,"score_gpt":0.3464767799808957,"score_spread":0.2942439982685873,"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."}}