{"id":"W4292466811","doi":"10.3389/fenvs.2022.949339","title":"Assessment of mercury enrichment in lake sediment records from Alberta Oil Sands development via fluvial and atmospheric pathways","year":2022,"lang":"en","type":"article","venue":"Frontiers in Environmental Science","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Environment and Parks; Suncor Energy Incorporated; Polar Knowledge Canada; Natural Resources Canada; Canadian Natural Resources Limited","keywords":"Fluvial; Environmental science; Oil sands; Sediment; Mercury (programming language); Aquatic ecosystem; Ecosystem; Floodplain; Hydrology (agriculture); Biota; Geology; Ecology; Asphalt; Oceanography; Geography; Structural basin; Archaeology","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":[0.0001820613,0.0002888459,0.0001330346,0.001640599,0.0009278697,0.0006025517,0.0003543936,0.0001765496,0.0004242708],"category_scores_gemma":[0.0002950744,0.0001729905,0.0001502871,0.001819276,0.0004470543,0.0001669492,0.0004636077,0.0001329438,0.00008884585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003486278,"about_ca_system_score_gemma":0.002594421,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8094336,"about_ca_topic_score_gemma":0.9319577,"domain_scores_codex":[0.9998698,0.000007113866,0.000008017274,0.00002196062,0.00006488102,0.00002821634],"domain_scores_gemma":[0.9997786,0.00001556355,0.0000483098,0.00000759248,0.0001177638,0.00003228843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008433445,0.00001667892,0.9804376,0.00002715567,0.00003193315,0.0002032723,0.0008704886,0.0003306402,0.009790439,0.00006665197,0.0001590121,0.007981561],"study_design_scores_gemma":[0.000001675738,0.000009180066,0.9983813,0.00000323091,0.000007324537,0.0000250538,0.0003237847,0.0002065689,0.0006914617,0.000007059971,0.0003411878,0.00000220302],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983715,0.00009270784,0.00008977586,0.00001334226,0.000001295229,0.00000651858,0.0003508648,0.00001341171,0.001060522],"genre_scores_gemma":[0.9979962,0.0001455803,0.0004368439,0.00001717613,0.000001765558,0.000006423333,0.0006452796,0.000003289709,0.0007473401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1905664,"threshold_uncertainty_score":0.3833774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006381027212906634,"score_gpt":0.2118914084873111,"score_spread":0.2055103812744045,"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."}}