{"id":"W2890430207","doi":"10.1021/acs.est.8b01246","title":"Updated Global and Oceanic Mercury Budgets for the United Nations Global Mercury Assessment 2018","year":2018,"lang":"en","type":"review","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":443,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Geological Survey of Canada; Natural Resources Canada","funders":"Division of Ocean Sciences; Canada Research Chairs; Natural Resources Canada; United Nations","keywords":"Mercury (programming language); Environmental science; Computer science","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":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0008817185,0.00061892,0.0007812777,0.0005516452,0.002068128,0.0001114798,0.001567583,0.0004109281,0.0006913443],"category_scores_gemma":[0.0001439529,0.0004279969,0.0002017579,0.006481954,0.009473004,0.0003672155,0.001916112,0.000330531,0.000469779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002219172,"about_ca_system_score_gemma":0.0001355632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009421911,"about_ca_topic_score_gemma":0.00007938551,"domain_scores_codex":[0.9963865,0.00008969424,0.0006504428,0.001172476,0.0007595096,0.0009413398],"domain_scores_gemma":[0.9983062,0.0001528015,0.0004693932,0.000831524,0.00001229947,0.0002277403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001201315,0.0004113829,0.005388773,0.000308415,0.0004065236,0.00001163393,0.0001404504,0.00001695877,0.0001580315,0.01352981,0.0379313,0.9416847],"study_design_scores_gemma":[0.000214729,0.0001795512,0.002975645,0.0001547387,0.000432224,0.0001411863,0.0002906188,0.00009514717,0.0000127278,0.0008554137,0.9941714,0.0004766172],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003997498,0.9786341,0.001065361,0.001959299,0.001193073,0.003950776,0.001429728,0.0003340607,0.007436062],"genre_scores_gemma":[0.01070435,0.9866624,0.001496485,0.0002419143,0.00007337184,0.0003486257,0.0001480351,0.00002889753,0.0002959306],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9562401,"threshold_uncertainty_score":0.9998172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02609948397738469,"score_gpt":0.336177290271525,"score_spread":0.3100778062941403,"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."}}