{"id":"W3126593038","doi":"10.1002/etc.5009","title":"Nearshore Sedimentary Mercury Concentrations Reflect Legacy Point Sources and Variable Sedimentation Patterns Under a Natural Recovery Strategy","year":2021,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Lawrence River Institute of Environmental Sciences; Queen's University","funders":"","keywords":"Mercury (programming language); Sedimentation; Sediment; Environmental science; Contamination; Sedimentary rock; Environmental remediation; Shore; Ecosystem; Hydrology (agriculture); Geology; Oceanography; Ecology; Geochemistry; Geomorphology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001167575,0.0001706522,0.0001071561,0.0005748292,0.0004747712,0.0004412104,0.0002122523,0.0001419401,0.00101526],"category_scores_gemma":[0.0002193032,0.0001465752,0.00007911306,0.0006135119,0.0004324202,0.000114637,0.0002155411,0.00009504649,0.0002367501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001887426,"about_ca_system_score_gemma":0.0008876006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4498545,"about_ca_topic_score_gemma":0.6236128,"domain_scores_codex":[0.9998629,0.00001023771,0.000006215045,0.00003431572,0.0000576728,0.00002864731],"domain_scores_gemma":[0.9998317,0.000008729176,0.00005558689,0.00001072854,0.00007564289,0.00001765971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002182774,0.00003271432,0.9159712,0.00002259678,0.00003618011,0.0001482271,0.0003717901,0.0004520793,0.07175054,0.00008278993,0.0003655999,0.01054804],"study_design_scores_gemma":[9.167622e-7,0.00001656789,0.9983454,8.944781e-7,0.000002721037,0.00002051125,0.00007953469,0.0001503554,0.00124353,0.000005588874,0.000132976,0.000001140957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985031,0.00004924804,0.0001511449,0.00000899637,6.464852e-7,0.000004281133,0.0002306307,0.00001101861,0.001041059],"genre_scores_gemma":[0.9986597,0.00003661858,0.0001956961,0.000007413736,5.697309e-7,0.000002760437,0.0002857893,0.000002648384,0.0008088676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4498545,"threshold_uncertainty_score":0.8944722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009583233988900533,"score_gpt":0.2386236560488114,"score_spread":0.2290404220599108,"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."}}