{"id":"W2042194572","doi":"10.1016/j.envpol.2013.01.024","title":"Modelling mercury concentrations in prey fish: Derivation of a national-scale common indicator of dietary mercury exposure for piscivorous fish and wildlife","year":2013,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; Queen's University","funders":"U.S. Geological Survey; Clean Air Regulatory Agenda","keywords":"Methylmercury; Bioaccumulation; Mercury (programming language); Environmental science; Forage fish; Wildlife; Fishery; Predation; Fish <Actinopterygii>; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.000679542,0.0004463164,0.0004008023,0.0003683014,0.000420457,0.0007509917,0.001038751,0.001189529,0.0006542993],"category_scores_gemma":[0.001692668,0.0004044466,0.0008392064,0.0005368988,0.0004775458,0.0007734203,0.0008045822,0.00053066,0.00008316476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001468357,"about_ca_system_score_gemma":0.001573538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1029578,"about_ca_topic_score_gemma":0.08630035,"domain_scores_codex":[0.9998628,0.00003959818,0.000007938987,0.00004586811,0.00001683518,0.00002689031],"domain_scores_gemma":[0.9995731,0.0002144462,0.00006693869,0.0000356776,0.0000799505,0.00002993504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001969038,0.00001794802,0.007555,0.00001327856,0.00003792908,0.00003792818,0.00002807268,0.9888259,0.0007470144,0.000951942,0.00006364169,0.001701743],"study_design_scores_gemma":[0.00000369074,0.00001204618,0.002121669,0.000001838941,0.00001316735,0.00001135261,0.00002410694,0.9969082,0.0002735901,0.0005629593,0.00006254324,0.000004804693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9250827,0.00006691099,0.07128046,0.0001640548,0.00001126114,0.00002664065,0.0002790549,0.00008846971,0.003000482],"genre_scores_gemma":[0.9837714,0.00004030295,0.01507726,0.00001891458,0.000002777182,0.00002796255,0.0001519612,0.00001999514,0.0008894237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1029578,"threshold_uncertainty_score":0.204717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781789268483661,"score_gpt":0.2343921911645733,"score_spread":0.2165742984797367,"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."}}