{"id":"W3033544833","doi":"10.1093/gigascience/giaa053","title":"Metagenomic analysis of planktonic riverine microbial consortia using nanopore sequencing reveals insight into river microbe taxonomy and function","year":2020,"lang":"en","type":"article","venue":"GigaScience","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University and Génome Québec Innovation Centre; McGill Genome Centre; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Division of Environmental Biology; National Institute of General Medical Sciences; Rijkswaterstaat; Medical Research Council; National Institutes of Health; Canada Research Chairs; Biotechnology and Biological Sciences Research Council; Rosetrees Trust; Canada Foundation for Innovation; National Science Foundation; Canadian Institutes of Health Research; Compute Canada; Oxford Nanopore Technologies; Genome Canada","keywords":"Metagenomics; Computational biology; Function (biology); Taxonomy (biology); Biological classification; Nanopore; Plankton; Biology; Nanopore sequencing; Computer science; Ecology; Evolutionary biology; DNA sequencing; Nanotechnology; Genetics; Gene; Materials science","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":[0.0003376273,0.0002683004,0.0002085183,0.0005068265,0.0002572486,0.0005249012,0.0001664963,0.0003163778,0.000374165],"category_scores_gemma":[0.0004019919,0.0001185864,0.0003730189,0.0004186312,0.000220624,0.0004421268,0.0004113308,0.0002843333,0.0001470896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001669615,"about_ca_system_score_gemma":0.0001722801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008390309,"about_ca_topic_score_gemma":0.001721988,"domain_scores_codex":[0.9998475,0.00002163687,0.00001140066,0.00006918611,0.00002822685,0.00002189388],"domain_scores_gemma":[0.9997746,0.00006400008,0.00004869752,0.00002993668,0.00005324284,0.00002944665],"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":[0.00009710936,0.00004158538,0.07257042,0.00009924978,0.00006924043,0.00005590004,0.000260491,0.0007086592,0.9130085,0.0001329606,0.00006447906,0.01289142],"study_design_scores_gemma":[0.000009276071,0.0005064465,0.7803914,0.00005026375,0.0001753076,0.0004807789,0.0007358082,0.01167096,0.2013034,0.0008304093,0.003806609,0.00003942313],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914555,0.000353848,0.006729688,0.00003866422,0.000004416992,0.00001598941,0.0008732119,0.00003870742,0.0004899915],"genre_scores_gemma":[0.9838406,0.0003202757,0.0138831,0.00005489638,0.000004169298,0.00002807991,0.001510063,0.00001412172,0.0003445829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008390309,"threshold_uncertainty_score":0.001785576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03684712461829864,"score_gpt":0.2243594071150118,"score_spread":0.1875122824967132,"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."}}