{"id":"W2181224535","doi":"10.1139/cjfas-2012-0338","title":"An overview of mercury concentrations in freshwater fish species: a national fish mercury dataset for Canada","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Department of Agriculture; Research Manitoba; University of Alberta; University of Victoria; Fisheries and Oceans Canada; Environment and Climate Change Canada; Ministry of the Environment, Conservation and Parks; Queen's University","funders":"","keywords":"Esox; Pike; Mercury (programming language); Biomagnification; Freshwater fish; Trout; Salvelinus; Fishery; MERCURY EXPOSURE; Biomonitoring; Environmental science; Methylmercury; Trophic level; Ecology; Biology; Fish <Actinopterygii>; Bioaccumulation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004348545,0.00008477785,0.0001828368,0.00006325209,0.0002250734,0.0001018331,0.0002261148,0.00002224722,0.002333809],"category_scores_gemma":[0.0002497354,0.00006633488,0.00002315749,0.0002188197,0.0006446676,0.0008124385,0.00001202644,0.00005165436,9.536722e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001156211,"about_ca_system_score_gemma":0.0009318643,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5661486,"about_ca_topic_score_gemma":0.9851996,"domain_scores_codex":[0.9989275,0.00003812296,0.0003663143,0.0001156518,0.0003237303,0.0002286946],"domain_scores_gemma":[0.9992971,0.0001433089,0.0001728194,0.00006341658,0.00003781506,0.0002854743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000003899728,0.0000188543,0.2884109,0.00002188618,0.00001688515,0.000005052908,0.002126133,0.0001497459,0.0005827915,0.000389854,0.7059483,0.002325669],"study_design_scores_gemma":[0.0006408176,0.0004676117,0.5389721,0.0001320013,0.00003038383,0.00004133365,0.01094585,0.003444095,0.00058371,0.00431002,0.4400726,0.0003595414],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858389,0.000234559,0.00003582367,0.01080235,0.0002218228,0.0002169592,0.001795068,9.563287e-7,0.0008535387],"genre_scores_gemma":[0.9961278,0.0001184944,0.001004117,0.002563429,0.00004064064,0.000008517252,0.00006018118,0.000003413143,0.00007342181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4190509,"threshold_uncertainty_score":0.9985782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05597448580549493,"score_gpt":0.2780655175209089,"score_spread":0.2220910317154139,"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."}}