{"id":"W2920049021","doi":"10.1016/j.envint.2019.02.043","title":"Mechanisms of algal biomass input enhanced microbial Hg methylation in lake sediments","year":2019,"lang":"en","type":"article","venue":"Environment International","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Government of Jiangsu Province; Salt Science Research Foundation","keywords":"Eutrophication; Environmental chemistry; Trophic level; Methylmercury; Algae; Algal bloom; Environmental science; Microbial loop; Biomass (ecology); Microbial food web; Bioaccumulation; Organic matter; Primary producers; Ecology; Water quality; Food web; Nutrient; Phytoplankton; Biology; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001980322,0.0006170598,0.000212421,0.0004290534,0.0002730833,0.0004224153,0.0002247549,0.0003859935,0.0006374038],"category_scores_gemma":[0.0001204358,0.0002148023,0.0003423928,0.0002119666,0.0003094931,0.0003445072,0.0006612064,0.0002875902,0.000213961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005480625,"about_ca_system_score_gemma":0.000409251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003009064,"about_ca_topic_score_gemma":0.002897744,"domain_scores_codex":[0.9998434,0.00002245938,0.00001325347,0.00004082909,0.00003575838,0.00004434049],"domain_scores_gemma":[0.9998878,0.00001090321,0.00003820136,0.000008880459,0.00003409855,0.00002012821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001367828,0.00002796012,0.008768002,0.0001706227,0.00002555888,0.000148901,0.0001132261,0.0002701458,0.9868505,0.000358582,0.00006425526,0.003065493],"study_design_scores_gemma":[0.00004145133,0.0005281281,0.2589958,0.00004346607,0.0001251212,0.0002141767,0.0005308189,0.005877426,0.7282239,0.001005923,0.004373136,0.00004064881],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933875,0.001792994,0.001733577,0.000187856,0.00002320839,0.00003992879,0.000378175,0.00008662345,0.002370037],"genre_scores_gemma":[0.997267,0.0005052124,0.0007640271,0.00004702482,0.000008207412,0.00002299009,0.0001299213,0.000006652171,0.001248857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003009064,"threshold_uncertainty_score":0.005983055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009022157283491527,"score_gpt":0.2364302201638141,"score_spread":0.2274080628803226,"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."}}