{"id":"W2937915575","doi":"10.1016/j.scitotenv.2019.04.101","title":"How closely do mercury trends in fish and other aquatic wildlife track those in the atmosphere? – Implications for evaluating the effectiveness of the Minamata Convention","year":2019,"lang":"en","type":"review","venue":"The Science of The Total Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":134,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada; University of Manitoba","funders":"Canada Research Chairs; National Science Foundation","keywords":"Biota; Environmental science; Mercury (programming language); Aquatic ecosystem; Wildlife; Ecology; Food chain; Climate change; Biogeochemical cycle; Environmental chemistry; Biology; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006761443,0.000272141,0.0005195183,0.00002284421,0.0004245198,0.00006541917,0.001591262,0.00006849851,0.00004277612],"category_scores_gemma":[0.0002871763,0.0001014196,0.0002941987,0.0007429653,0.002446886,0.000171783,0.0005985078,0.0002412194,0.000006419229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002346404,"about_ca_system_score_gemma":0.00005227955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001168544,"about_ca_topic_score_gemma":0.00002607957,"domain_scores_codex":[0.9968035,0.001286451,0.0004745155,0.0004043515,0.0007189917,0.0003122522],"domain_scores_gemma":[0.9968352,0.001318365,0.000664916,0.001147607,0.000005157241,0.00002875154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00008547643,0.0005823117,0.002966233,0.002460354,0.0001771238,1.629924e-7,0.009747602,0.007999875,0.003384936,0.002656577,0.000679614,0.9692597],"study_design_scores_gemma":[0.002676861,0.0009767646,0.8890229,0.01115249,0.003121888,0.0000999596,0.006598454,0.004794705,0.0007672402,0.009557672,0.0697087,0.001522298],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7350069,0.2406256,0.00005167049,0.009348558,0.0005297183,0.01187786,0.0002933021,0.00001086011,0.002255514],"genre_scores_gemma":[0.8610247,0.1356887,0.0001216034,0.0002389486,0.0000441799,0.001306559,0.000007323181,0.00005115708,0.001516743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9677374,"threshold_uncertainty_score":0.9015653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07902979342795398,"score_gpt":0.3562386051438325,"score_spread":0.2772088117158786,"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."}}