{"id":"W2325939713","doi":"10.1021/es403363v","title":"Low and Declining Mercury in Arctic Russian Rivers","year":2013,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security","funders":"National Research Council Canada; Kajima Foundation; International Atomic Energy Agency; National Science Foundation","keywords":"Arctic; Mercury (programming language); Environmental science; Watershed; The arctic; Structural basin; Climate change; Arctic ecology; Drainage basin; Physical geography; Oceanography; Geography; Geology","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":[0.0003128755,0.0001073285,0.0002199935,0.0008031091,0.0005047618,0.0006125596,0.0001949889,0.0002058158,0.000328024],"category_scores_gemma":[0.000393874,0.0001514483,0.000182085,0.0007512054,0.000523252,0.0002203388,0.0004340933,0.0002018985,0.00007705053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007486698,"about_ca_system_score_gemma":0.0006617776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03316468,"about_ca_topic_score_gemma":0.06256512,"domain_scores_codex":[0.9997595,0.00003615657,0.00002040905,0.00009289093,0.0000461215,0.00004490862],"domain_scores_gemma":[0.9997012,0.00003658559,0.0001290336,0.00001730005,0.00007804978,0.00003776452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000113466,0.00001432765,0.9782557,0.00003124147,0.00005321813,0.0001810085,0.001333271,0.0002290959,0.01402975,0.0002104455,0.0001087603,0.005439689],"study_design_scores_gemma":[9.74212e-7,0.00001353272,0.9987685,0.000004643523,0.00001003611,0.00005379012,0.0004450737,0.00005741464,0.0002421961,0.00002147553,0.0003801715,0.000002110724],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999115,0.0001566024,0.0000603827,0.00002272091,0.000001539512,8.759745e-7,0.00007572791,0.000004150092,0.0005628369],"genre_scores_gemma":[0.9992998,0.0001137266,0.0001030987,0.00001728543,0.00000215637,0.00000220438,0.000137391,0.000002225926,0.0003220694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03316468,"threshold_uncertainty_score":0.0659433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006783792981198289,"score_gpt":0.2260474992487161,"score_spread":0.2192637062675178,"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."}}