{"id":"W2278929876","doi":"10.1016/j.scitotenv.2016.01.061","title":"Trends in mercury wet deposition and mercury air concentrations across the U.S. and Canada","year":2016,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":139,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Mercury (programming language); Deposition (geology); Environmental science; Trend analysis; Sulfate; Environmental chemistry; Physical geography; Atmospheric sciences; Geography; Chemistry; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003045596,0.000244782,0.0002252159,0.002262499,0.001096712,0.001234852,0.000652523,0.0003864145,0.002683095],"category_scores_gemma":[0.000927525,0.0002202598,0.0004506411,0.007129454,0.0004649728,0.0004984185,0.0005279459,0.0005690904,0.0002651916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02162553,"about_ca_system_score_gemma":0.02924046,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9971887,"about_ca_topic_score_gemma":0.9985319,"domain_scores_codex":[0.9994687,0.00002410125,0.00002871045,0.00007719038,0.0002468145,0.0001544512],"domain_scores_gemma":[0.9984115,0.00006108721,0.0001307513,0.0000189703,0.001213588,0.0001639785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001643522,0.0000326392,0.9573473,0.0001544975,0.0002716479,0.0001149617,0.001099224,0.001057929,0.001190177,0.001528032,0.01208751,0.02495176],"study_design_scores_gemma":[0.000004324484,0.00001073699,0.9913445,0.0000291141,0.00004822321,0.00003078927,0.00113191,0.0006138331,0.0002514878,0.00007079833,0.006453821,0.00001056236],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9370398,0.004526248,0.000377025,0.00326714,0.00006484843,0.00003040834,0.038173,0.0001174634,0.01640409],"genre_scores_gemma":[0.9818117,0.003051547,0.000484802,0.0002590978,0.00001744046,0.00001197637,0.006907008,0.00001561996,0.007440793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02162553,"threshold_uncertainty_score":0.1569049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00886818822654711,"score_gpt":0.2282593344628109,"score_spread":0.2193911462362638,"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."}}