{"id":"W2923087347","doi":"10.1016/j.scitotenv.2019.03.424","title":"Mercury speciation and mercury stable isotope composition in sediments from the Canadian Arctic Archipelago","year":2019,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University","funders":"Networks of Centres of Excellence of Canada; Natural Sciences and Engineering Research Council of Canada; ArcticNet","keywords":"Mercury (programming language); Arctic; Environmental chemistry; Isotope; Loss on ignition; Sediment; MERCURE; Archipelago; Stable isotope ratio; Chemistry; Geology; Environmental science; Oceanography; Mineralogy; Analytical Chemistry (journal); Geomorphology","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":[0.0002320291,0.0004159363,0.0003377628,0.003421901,0.005192616,0.00141497,0.0005845267,0.0004232042,0.0009428086],"category_scores_gemma":[0.0003764932,0.0003467347,0.0003008449,0.00365291,0.0007872956,0.0003314925,0.0005865493,0.0003542803,0.0001955357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01366719,"about_ca_system_score_gemma":0.01037366,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9880862,"about_ca_topic_score_gemma":0.9942274,"domain_scores_codex":[0.9997883,0.00000791811,0.00001123662,0.0000488175,0.00007350269,0.00007022484],"domain_scores_gemma":[0.9997399,0.00001092088,0.0000183538,0.000005692769,0.0001905699,0.00003446616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001192126,0.0001212872,0.8541762,0.0002491415,0.0005523051,0.0008379073,0.004264047,0.00266644,0.09563012,0.0005310223,0.001528902,0.03825052],"study_design_scores_gemma":[0.000006081902,0.00000981434,0.9944279,0.00001161165,0.00004452181,0.00007283518,0.001178796,0.0002902553,0.002177184,0.00004372881,0.001724144,0.00001309054],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963127,0.0006935703,0.00006524313,0.00006969684,0.000005624424,0.000005969483,0.0008611194,0.000009955606,0.001976073],"genre_scores_gemma":[0.9966745,0.000624663,0.0002792812,0.00005453029,0.000002862809,0.000004327585,0.000859216,0.000008703571,0.001491964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01366719,"threshold_uncertainty_score":0.09916294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025401992626259,"score_gpt":0.2101511959987193,"score_spread":0.1998971760724567,"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."}}