{"id":"W3033217506","doi":"10.1101/2020.06.03.132969","title":"Mercury methylation by metabolically versatile and cosmopolitan marine bacteria","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia","funders":"Medical Research Council; Office of Science; University of Melbourne; National Health and Medical Research Council; Natural Sciences and Engineering Research Council of Canada; Woods Hole Oceanographic Institution; State Government of Victoria; Joint Genome Institute; U.S. Department of Energy","keywords":"Methylmercury; Mercury (programming language); Methylation; Marine bacteriophage; Bacteria; Anoxic waters; Environmental chemistry; Gene; Biology; DNA methylation; Microorganism; Chemistry; Gene expression; Genetics; Bioaccumulation","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":[0.0001044938,0.0003069642,0.0001488108,0.0003927759,0.0003505095,0.0004943532,0.0001892211,0.0001985782,0.0002664212],"category_scores_gemma":[0.0001503371,0.0001197264,0.0001782772,0.0005043811,0.0002365056,0.0001198062,0.0004673601,0.0001897319,0.0001320677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000586705,"about_ca_system_score_gemma":0.0005653285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04958101,"about_ca_topic_score_gemma":0.07279108,"domain_scores_codex":[0.9998084,0.00001055391,0.000007463033,0.00007632566,0.0000595566,0.00003768085],"domain_scores_gemma":[0.9999442,0.00000565098,0.00001377324,0.000005521655,0.00001675459,0.00001398231],"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":[0.0002494095,0.00008027205,0.09527448,0.00006532995,0.00003889926,0.0001044808,0.0002931037,0.0009202393,0.8935929,0.0001176713,0.0001295171,0.009133741],"study_design_scores_gemma":[0.00002041438,0.0002357055,0.8822291,0.00002109571,0.00006018603,0.0002223059,0.001203183,0.006349138,0.1064127,0.0001637034,0.003061363,0.00002095739],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988723,0.0001024272,0.0001991512,0.00001342049,0.000001482274,0.000005305959,0.0003280965,0.000008182245,0.0004695879],"genre_scores_gemma":[0.9976057,0.0001117155,0.0009075375,0.00001430618,0.000001509296,0.000008356154,0.0007992851,0.000003602784,0.0005479447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04958101,"threshold_uncertainty_score":0.09858483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01450524544320125,"score_gpt":0.2284434932621375,"score_spread":0.2139382478189362,"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."}}