{"id":"W1994991458","doi":"10.1016/j.apgeochem.2011.06.006","title":"Total Hg concentrations in stream and lake sediments: Discerning geospatial patterns and controls across Canada","year":2011,"lang":"en","type":"article","venue":"Applied Geochemistry","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada; University of New Brunswick","funders":"Natural Resources Canada","keywords":"STREAMS; Environmental science; Drainage basin; Sediment; Mercury (programming language); Hydrology (agriculture); Physical geography; Deposition (geology); Geospatial analysis; Geological survey; Sampling (signal processing); Structural basin; Geology; Geography; Geomorphology; Remote sensing","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":[0.0003184838,0.0002041864,0.0002247727,0.002341799,0.00223994,0.00152662,0.0005258797,0.0002535356,0.0009196785],"category_scores_gemma":[0.0009907344,0.0002802099,0.0002742598,0.004556272,0.001006796,0.000348847,0.0006598155,0.0002992586,0.0000893749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02009808,"about_ca_system_score_gemma":0.01729357,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9963306,"about_ca_topic_score_gemma":0.9984406,"domain_scores_codex":[0.9995447,0.00003395529,0.00002652212,0.00009462721,0.0001599369,0.0001402003],"domain_scores_gemma":[0.9990332,0.0000682554,0.0001231549,0.0000230466,0.0006309027,0.0001213983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001815355,0.00002460282,0.9815513,0.00003426072,0.00009675664,0.00008655461,0.002026698,0.0007501963,0.003019891,0.0003967995,0.0005825956,0.01124878],"study_design_scores_gemma":[0.000002248872,0.000005886174,0.9975516,0.000006759978,0.00001841949,0.00001424365,0.001220105,0.000333473,0.0002474184,0.00003417004,0.0005604943,0.000005271936],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972913,0.0002549111,0.0001138582,0.00008756406,0.000002583638,0.000009282916,0.0009693336,0.00000875869,0.001262514],"genre_scores_gemma":[0.9975874,0.0002775868,0.0002164429,0.00002110371,0.000001309038,0.000003675542,0.0004464476,0.000004738702,0.001441275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02009808,"threshold_uncertainty_score":0.1458224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008759374037835036,"score_gpt":0.2146388346424661,"score_spread":0.2058794606046311,"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."}}