{"id":"W2583727975","doi":"10.1002/2016gb005452","title":"Development of a global ocean mercury model with a methylation cycle: Outstanding issues","year":2017,"lang":"en","type":"article","venue":"Global Biogeochemical Cycles","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Biogeochemistry; Oceanography; Sediment trap; Geochemical cycle; Environmental science; Mercury (programming language); Biogeochemical cycle; Carbon cycle; Seabed; Water column; Environmental chemistry; Geology; Chemistry; Ecology; Ecosystem","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001694867,0.0001925178,0.0002417541,0.000007560187,0.000360262,0.00005465584,0.0003180742,0.00008010852,0.00008355216],"category_scores_gemma":[0.00009121115,0.0001494308,0.00005745926,0.0001095696,0.0004141071,0.0002935349,0.000316254,0.00005069825,0.0000457382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002796353,"about_ca_system_score_gemma":0.00003175309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002700207,"about_ca_topic_score_gemma":0.0001634476,"domain_scores_codex":[0.9986751,0.00001565478,0.0002778265,0.0002946464,0.0004430374,0.0002937384],"domain_scores_gemma":[0.9992862,0.00001078954,0.0002241386,0.0003145226,0.00002335977,0.0001409793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001324718,0.0001178268,0.9436498,0.00002559698,0.0001037654,0.000003852175,0.0007388233,0.0003091554,0.03655202,0.001369726,0.002369262,0.01462765],"study_design_scores_gemma":[0.002106791,0.0001122324,0.6412663,0.0002520728,0.0002057215,0.00002842593,0.001542789,0.005224149,0.2984704,0.04502114,0.004405457,0.001364537],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986769,0.0001100168,0.001056526,0.0005395801,0.00003268686,0.0001297218,0.0001703135,0.00004315369,0.01114898],"genre_scores_gemma":[0.9740787,0.00001630826,0.02579472,0.00004484737,0.00001818776,0.000004162255,0.0000211143,0.000004961559,0.00001703204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3023836,"threshold_uncertainty_score":0.6093609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02676018988902142,"score_gpt":0.3040673965679076,"score_spread":0.2773072066788862,"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."}}