{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008915696,0.0002299609,0.0003115599,0.00006209257,0.0003918365,0.00001874438,0.0004267013,0.00003579677,0.002547549],"category_scores_gemma":[0.00001355466,0.0002315189,0.00003602964,0.000582386,0.0008164205,0.0003349086,0.001186278,0.0002182913,0.00001081723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001697297,"about_ca_system_score_gemma":0.0000639987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009012612,"about_ca_topic_score_gemma":0.0009013652,"domain_scores_codex":[0.9971256,0.000112985,0.0005139517,0.0006604759,0.00109416,0.0004928483],"domain_scores_gemma":[0.9993504,0.00005017188,0.000178957,0.0002532679,0.000001452718,0.0001657344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001958282,0.00031564,0.9219083,0.000003846941,0.00001008312,0.00000707938,0.003986201,0.001573523,0.0209188,0.000005605183,0.0003327049,0.05091862],"study_design_scores_gemma":[0.0007786203,0.0001274437,0.9779143,0.00001175418,0.000008407119,0.000003226653,0.003051252,0.004136005,0.001866245,0.0001319834,0.01167087,0.0002999407],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939827,0.0002219679,0.0005444108,0.00007875269,0.0006071052,0.0002102682,0.00004076616,0.000007961827,0.004306089],"genre_scores_gemma":[0.9778118,0.0001882015,0.02131144,0.0001645557,0.000009831841,0.0001360033,0.00002603342,0.00001155266,0.0003406325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05600594,"threshold_uncertainty_score":0.9983643,"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."}}