{"id":"W4229442078","doi":"10.1039/d2em00064d","title":"Mercury methylation and methylmercury demethylation in boreal lake sediment with legacy sulphate pollution","year":2022,"lang":"en","type":"article","venue":"Environmental Science Processes & Impacts","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Methylmercury; Sediment; Environmental chemistry; Demethylation; Aquatic ecosystem; Pollution; Environmental science; Mercury (programming language); Eutrophication; Organic matter; Hydrology (agriculture); Ecology; Chemistry; Nutrient; Geology; Bioaccumulation; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001655126,0.0003150062,0.0001758133,0.0002509402,0.0003742349,0.0003627233,0.0001881392,0.0002822194,0.0005940744],"category_scores_gemma":[0.000237532,0.0001760309,0.0002135607,0.0002843788,0.0002658774,0.000294044,0.0003715761,0.000184863,0.00006721936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004779824,"about_ca_system_score_gemma":0.0004021073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01915796,"about_ca_topic_score_gemma":0.04127206,"domain_scores_codex":[0.9998291,0.00002783386,0.00001893725,0.00005085931,0.00003741935,0.00003581096],"domain_scores_gemma":[0.9998727,0.000009043261,0.00003900671,0.000009022458,0.00003925577,0.00003103611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001382514,0.0001130042,0.09111851,0.0001060993,0.0001182371,0.0001161993,0.0003940213,0.0003534271,0.9004981,0.00005109436,0.0001193887,0.005629471],"study_design_scores_gemma":[0.0000235485,0.001237052,0.8988759,0.000008431325,0.0000956923,0.0001672182,0.0003870443,0.0009609364,0.09711356,0.00006412504,0.00105219,0.00001431129],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993057,0.0001429735,0.00007073579,0.00001120366,0.00000422372,0.000003767604,0.0001576161,0.000005414841,0.0002983589],"genre_scores_gemma":[0.9986468,0.00008885103,0.0002192277,0.00001954471,0.000003582623,0.000006171944,0.0003253921,0.00000244118,0.0006880359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01915796,"threshold_uncertainty_score":0.03809291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01056814538946581,"score_gpt":0.2465266622729574,"score_spread":0.2359585168834916,"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."}}