{"id":"W4226018358","doi":"10.21203/rs.3.rs-1125389/v1","title":"Characterization and Simulation of Metagenomic Nanopore Sequencing Data with Meta-NanoSim","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","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; Nanopore; Nanopore sequencing; Computational biology; Characterization (materials science); Computer science; Biology; DNA sequencing; Nanotechnology; Materials science; Genetics; 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":[0.0008535481,0.0009484351,0.0005586594,0.0005774769,0.0005283207,0.0006355658,0.001506264,0.001282733,0.001623607],"category_scores_gemma":[0.00213003,0.0004793454,0.001336389,0.0005864255,0.0004637564,0.0006078456,0.0007690086,0.001044499,0.0002801298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009491036,"about_ca_system_score_gemma":0.001138928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009196792,"about_ca_topic_score_gemma":0.006553341,"domain_scores_codex":[0.9997688,0.00006217314,0.0000164195,0.00005787602,0.00005535735,0.00003946873],"domain_scores_gemma":[0.9988382,0.0007398698,0.00007569784,0.0001195672,0.0001438779,0.00008281661],"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":[0.0001285903,0.00008356785,0.00733597,0.00008631964,0.0000738724,0.0000992994,0.00008091775,0.9814048,0.005005957,0.001360795,0.0005207846,0.003818981],"study_design_scores_gemma":[0.000005932528,0.00001502661,0.0002829869,0.00000276044,0.000004551391,0.000007544842,0.000009037644,0.9974407,0.001754061,0.0002584567,0.0002145569,0.000004495595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"software","genre_scores_codex":[0.8331196,0.000309199,0.151609,0.0003936968,0.0001173223,0.0001529838,0.003719265,0.006904989,0.003673985],"genre_scores_gemma":[0.9093373,0.0001332544,0.08612421,0.0001323982,0.00001719069,0.0002337393,0.002887092,0.000449193,0.0006855025],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.009196792,"threshold_uncertainty_score":0.01828653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1423640399839518,"score_gpt":0.3764803593662773,"score_spread":0.2341163193823255,"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."}}