{"id":"W2952788979","doi":"10.1101/015552","title":"A complete bacterial genome assembled <i>de novo</i> using only nanopore sequencing data","year":2015,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"Medical Research Council; Surgical Reconstruction and Microbiology Research Centre; Government of Ontario; Ontario Institute for Cancer Research; National Institute for Health and Care Research; Oxford Nanopore Technologies","keywords":"Minion; Contig; Nanopore sequencing; Sequence assembly; Nanopore; Hybrid genome assembly; Reference genome; Bacterial genome size; Computational biology; Genome; Sequence (biology); Hidden Markov model; Computer science; Biology; Algorithm; Genetics; Gene; Artificial intelligence; Nanotechnology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000950281,0.0006813618,0.0006765908,0.0001071862,0.0002181152,0.0002279984,0.001482076,0.0006521721,0.00001155471],"category_scores_gemma":[0.0002072533,0.000769013,0.0001424042,0.000168828,0.0001189929,0.000005055974,0.003539033,0.0004163059,0.00001335271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003093735,"about_ca_system_score_gemma":0.003270524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001353157,"about_ca_topic_score_gemma":0.00002515978,"domain_scores_codex":[0.9966099,0.00017963,0.0005930609,0.00154587,0.0002882784,0.0007832076],"domain_scores_gemma":[0.9957548,0.00001658589,0.0004687957,0.002864127,0.0005591369,0.0003365433],"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":[0.00006104638,0.00004722409,0.001792412,0.0001386273,0.0003566515,0.00003680604,0.00001188227,0.0004145298,0.9968903,0.00001795229,0.0002316095,9.437401e-7],"study_design_scores_gemma":[0.003218287,0.000401804,0.02284432,0.0004392459,0.0009531754,0.00000210759,0.00003696111,0.003069155,0.788419,0.00001357745,0.1762409,0.004361401],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911739,0.002598428,0.001309042,0.00005374604,0.001251266,0.0005589896,0.002994855,0.00003942914,0.00002035891],"genre_scores_gemma":[0.9805927,0.0004639276,0.01642642,0.0002173218,0.002053258,0.00003476461,0.00002890181,0.000177784,0.000004955104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2084713,"threshold_uncertainty_score":0.9994761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05509866015654628,"score_gpt":0.2567787942508405,"score_spread":0.2016801340942943,"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."}}