{"id":"W4327896682","doi":"10.1093/gigascience/giad013","title":"Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim","year":2023,"lang":"en","type":"article","venue":"GigaScience","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Genome British Columbia; University of British Columbia","funders":"National Human Genome Research Institute; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Genome British Columbia; Genome Canada","keywords":"Metagenomics; Computational biology; Nanopore; Computer science; Characterization (materials science); Nanopore sequencing; Data science; Biology; DNA sequencing; Nanotechnology; Genetics; Materials science; Gene","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":[0.0008146173,0.001150111,0.0006803488,0.0006273728,0.0005295951,0.000767276,0.001750076,0.001464501,0.002141849],"category_scores_gemma":[0.002413783,0.000589459,0.00156325,0.0006640545,0.0004812966,0.0006902826,0.0008788761,0.001295149,0.0004220048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006846,"about_ca_system_score_gemma":0.001383664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01013101,"about_ca_topic_score_gemma":0.00859676,"domain_scores_codex":[0.9997466,0.00006185106,0.00001992807,0.00006342999,0.0000650844,0.00004316417],"domain_scores_gemma":[0.9989298,0.0006839086,0.00007093436,0.0001126377,0.0001289874,0.00007373319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001584032,0.0001015062,0.00766171,0.0001916263,0.0001151164,0.0001404182,0.0001331897,0.9758002,0.006840074,0.002241608,0.001279107,0.0053371],"study_design_scores_gemma":[0.00001094042,0.00001963818,0.000324233,0.000005843278,0.00000795164,0.00001266983,0.00001453634,0.9960672,0.002365736,0.0005868982,0.0005768151,0.000007543842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7530722,0.0007159224,0.2144781,0.0007589767,0.0002309401,0.0002645162,0.009661119,0.0139005,0.006917726],"genre_scores_gemma":[0.8441195,0.0003163297,0.1444361,0.0002867093,0.00003311295,0.0004888655,0.007777226,0.001216455,0.001325749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01013101,"threshold_uncertainty_score":0.02014405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0653916397109796,"score_gpt":0.2779367005378232,"score_spread":0.2125450608268436,"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."}}