{"id":"W2950671516","doi":"10.1093/gigascience/giz043","title":"Ultra-deep, long-read nanopore sequencing of mock microbial community standards","year":2019,"lang":"en","type":"article","venue":"GigaScience","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":341,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of British Columbia; Northeastern University; Medical Research Council; University of Birmingham; Ontario Institute for Cancer Research; National Institute for Health and Care Research; Oxford Nanopore Technologies; University of Nottingham; University of Queensland; Cornell University","keywords":"Metagenomics; Nanopore sequencing; Deep sequencing; Sequence assembly; Computational biology; Microbial population biology; Illumina dye sequencing; DNA sequencing; Biology; Nanopore; Computer science; Genome; Data mining; Genetics; Gene; Nanotechnology; Bacteria","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001482098,0.0007125387,0.000625097,0.0006455883,0.0006382889,0.0009347524,0.0009178883,0.0009383167,0.0009038688],"category_scores_gemma":[0.002955412,0.0002910258,0.0006596549,0.000767872,0.0005221696,0.001066791,0.001184642,0.0009609437,0.0006474396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005044554,"about_ca_system_score_gemma":0.0004515325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008922959,"about_ca_topic_score_gemma":0.001847707,"domain_scores_codex":[0.9986106,0.0002291554,0.0001721417,0.000372471,0.0004960966,0.0001195171],"domain_scores_gemma":[0.9980824,0.0003981267,0.0002793412,0.000348936,0.0007150432,0.0001761341],"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":[0.0002080409,0.00008583388,0.00607365,0.0001516389,0.00003379576,0.00004341403,0.0001548872,0.001415203,0.9857455,0.0003773035,0.0002036037,0.005507025],"study_design_scores_gemma":[0.00003279858,0.000583722,0.04341711,0.00007523465,0.00007954689,0.0003285447,0.0002734055,0.02072073,0.9229415,0.001794565,0.009668455,0.00008441501],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8426853,0.00038842,0.1415958,0.000179651,0.00006571425,0.0003360675,0.01180393,0.0009494532,0.001995617],"genre_scores_gemma":[0.6137729,0.0003935392,0.3349749,0.0003795455,0.00003912443,0.0009319778,0.0472989,0.0003841931,0.001824967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001482098,"threshold_uncertainty_score":0.00783819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169140648218181,"score_gpt":0.2436705306539455,"score_spread":0.2319791241717637,"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."}}