{"id":"W2347156000","doi":"10.1016/j.scitotenv.2016.03.031","title":"Assessing potential health risks to fish and humans using mercury concentrations in inland fish from across western Canada and the United States","year":2016,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"U.S. Geological Survey; Clean Air Regulatory Agenda; Government of Canada; U.S. Environmental Protection Agency","keywords":"Predatory fish; Fish <Actinopterygii>; Environmental science; Mercury (programming language); Fish consumption; Diversity of fish; Contamination; Fish products; Fishery; Biology; Ecology; Computer science","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":[0.0004198608,0.0004561502,0.0002683636,0.00143918,0.002053382,0.001082197,0.0005435414,0.0003160558,0.0005542989],"category_scores_gemma":[0.0007736731,0.0001934814,0.0002944955,0.003417083,0.0005752413,0.000255548,0.0005111276,0.0002850311,0.00009296846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01177876,"about_ca_system_score_gemma":0.01422617,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9933552,"about_ca_topic_score_gemma":0.9970406,"domain_scores_codex":[0.9996986,0.00004330802,0.00001360478,0.00003795028,0.0001274717,0.00007898836],"domain_scores_gemma":[0.9994202,0.00004621224,0.00005839007,0.00001212655,0.0004025854,0.00006061846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001366902,0.00005649297,0.982309,0.00004626546,0.0001600659,0.000176495,0.001815683,0.001062842,0.002619449,0.000131552,0.0003275131,0.01115797],"study_design_scores_gemma":[0.000005746168,0.00006589714,0.9901924,0.00002726885,0.0001239849,0.00007106951,0.005828339,0.0008109295,0.001442363,0.00007966212,0.001341709,0.00001055389],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970657,0.0001927033,0.0002117045,0.0000760204,0.000002006865,0.00002241698,0.0005382816,0.000004845142,0.001886352],"genre_scores_gemma":[0.9964477,0.00055778,0.0007369986,0.00007297406,0.000001622887,0.00001365743,0.00045707,0.000003436136,0.001708698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01177876,"threshold_uncertainty_score":0.08546126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03193436468188707,"score_gpt":0.2985481975620547,"score_spread":0.2666138328801676,"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."}}