{"id":"W2597163595","doi":"10.1002/etc.3757","title":"Mercury in tunas and blue marlin in the North Pacific Ocean","year":2017,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas","funders":"University of Michigan","keywords":"Tuna; Mercury (programming language); Yellowfin tuna; Albacore; Fishery; Swordfish; Environmental science; Scombridae; Pacific ocean; Oceanography; Biology; Fish <Actinopterygii>; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003240655,0.000215494,0.0001678649,0.001326588,0.000395041,0.0004562165,0.0002323518,0.0002448185,0.000306364],"category_scores_gemma":[0.0006153484,0.0001931992,0.0002694434,0.001171902,0.0002206705,0.0003311444,0.0004259232,0.0001629359,0.00008221224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000734218,"about_ca_system_score_gemma":0.0004592543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1642487,"about_ca_topic_score_gemma":0.2637998,"domain_scores_codex":[0.9998713,0.00001921872,0.00001317553,0.00004323259,0.0000297131,0.00002331781],"domain_scores_gemma":[0.9996735,0.0000270974,0.000160275,0.00001913559,0.00006923108,0.00005072972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000306724,0.000005136359,0.9965383,0.000007714758,0.00004357985,0.00006027869,0.0002170406,0.00005224327,0.0006243617,0.00001071079,0.00004595076,0.002364048],"study_design_scores_gemma":[9.663322e-7,0.000009606239,0.999286,0.000004088749,0.00001378513,0.00004443507,0.0001702047,0.0001095655,0.00008805077,0.000005916776,0.0002661379,0.000001281498],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993793,0.0001432916,0.00002042439,0.00002120714,0.000002113457,0.000001036078,0.0001891099,0.000003093366,0.0002404413],"genre_scores_gemma":[0.9988078,0.0002548402,0.0001814919,0.00002981398,0.000005942405,0.000005048133,0.0004174285,0.000003064511,0.000294528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1642487,"threshold_uncertainty_score":0.3265852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027292252455801,"score_gpt":0.2292953132176676,"score_spread":0.2190223906931096,"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."}}