{"id":"W3112156306","doi":"10.3390/foods9121824","title":"Identification of Arctic Food Fish Species for Anthropogenic Contaminant Testing Using Geography and Genetics","year":2020,"lang":"en","type":"article","venue":"Foods","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Carleton University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Northern Contaminants Program; Ontario Genomics; Genome Canada","keywords":"Fish migration; Arctic char; Salvelinus; Arctic; Trout; Fishery; Fishing; Congener; Pollutant; Brown trout; Population; Ecology; Marine Strategy Framework Directive; Environmental science; Grayling; Geography; Biology; Fish <Actinopterygii>; Environmental chemistry; Ecosystem; Chemistry","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":[0.0003966973,0.0003776025,0.0002671635,0.001882533,0.0007533855,0.0005434715,0.0002954409,0.0003067913,0.0009438841],"category_scores_gemma":[0.0006699344,0.0001634516,0.000305646,0.001324097,0.0003696122,0.0003132752,0.0005133369,0.0002097289,0.0005090221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005969338,"about_ca_system_score_gemma":0.0008394883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04538099,"about_ca_topic_score_gemma":0.09725945,"domain_scores_codex":[0.9997054,0.00006159968,0.00001904582,0.0001100824,0.00005929647,0.00004454683],"domain_scores_gemma":[0.999508,0.00005050412,0.0001843345,0.00002704209,0.000169365,0.0000608411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001238207,0.00004988056,0.8681399,0.0001308133,0.00006585791,0.0004007536,0.001465411,0.0005047587,0.0943289,0.0003370309,0.0002643858,0.03418838],"study_design_scores_gemma":[0.000003387104,0.0001157448,0.9915484,0.00003450451,0.00003977649,0.0003107156,0.001649024,0.0009306551,0.003030881,0.0001672323,0.002154274,0.00001552245],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872794,0.0004602987,0.006209581,0.0001121671,0.00001733902,0.00009064208,0.000899953,0.00004387932,0.004886772],"genre_scores_gemma":[0.9724496,0.000525421,0.023448,0.00008713348,0.00001160948,0.00009447188,0.001177071,0.00001641105,0.00219028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04538099,"threshold_uncertainty_score":0.09023368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07560930976369078,"score_gpt":0.2864674762138211,"score_spread":0.2108581664501303,"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."}}