{"id":"W4221048977","doi":"10.1073/pnas.2119857119","title":"Multidecadal declines in particulate mercury and sediment export from Russian rivers in the pan-Arctic basin","year":2022,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Wildlife Fund Canada; University of Alberta","funders":"","keywords":"Mercury (programming language); Arctic; Particulates; Environmental science; Methylmercury; Pollution; The arctic; Sediment; Drainage basin; Structural basin; Oceanography; Particulate pollution; Hydrology (agriculture); Environmental chemistry; Geology; Geography; Ecology; Chemistry; Bioaccumulation; Geomorphology","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":[0.0003198964,0.0001626125,0.0001904159,0.0004565998,0.0002975979,0.0005166193,0.0001466551,0.0001816426,0.0004934352],"category_scores_gemma":[0.0003508705,0.0001143317,0.0002599497,0.0005570621,0.0001604154,0.0003273074,0.0005623724,0.0002439742,0.0000880212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003198055,"about_ca_system_score_gemma":0.0003389741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02864537,"about_ca_topic_score_gemma":0.0395104,"domain_scores_codex":[0.9998803,0.00001377746,0.0000152844,0.00005250458,0.00001851562,0.00001969532],"domain_scores_gemma":[0.9997508,0.00002698018,0.00009788367,0.00003060449,0.00006687568,0.00002691382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007891295,0.00002412622,0.9860341,0.00003443328,0.0001913718,0.0000846074,0.0003984089,0.0008348381,0.003563891,0.0001238711,0.0004268363,0.008204652],"study_design_scores_gemma":[8.909478e-7,0.000006382629,0.9988227,0.000003291432,0.00001819626,0.00001942887,0.0001084486,0.000423131,0.00017441,0.0000139855,0.0004072216,0.000001845566],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985936,0.0001094534,0.0000930614,0.00004317751,0.000004058897,9.190169e-7,0.0007899461,0.00001307793,0.0003526354],"genre_scores_gemma":[0.9981225,0.0001311788,0.0001222183,0.00001655145,0.000005129646,0.000003210121,0.001296286,0.000004395413,0.0002983664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02864537,"threshold_uncertainty_score":0.05695724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03845868671836678,"score_gpt":0.2977436270007061,"score_spread":0.2592849402823393,"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."}}