{"id":"W2950266744","doi":"10.1093/gigascience/gix010","title":"NanoSim: nanopore sequence read simulator based on statistical characterization","year":2017,"lang":"en","type":"article","venue":"GigaScience","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":279,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Human Genome Research Institute; National Institutes of Health; University of British Columbia; Genome British Columbia; Genome Canada","keywords":"Nanopore; Computer science; Sequence (biology); Characterization (materials science); Nanopore sequencing; Computational biology; Nanotechnology; Biology; DNA sequencing; Materials science; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001477032,0.0001071996,0.00009011396,0.00001786365,0.0004731875,0.00008810231,0.0003971598,0.000058028,0.00001467755],"category_scores_gemma":[0.0003226128,0.00009943149,0.00002860236,0.00002782008,0.0002961153,0.000001920209,0.0001092498,0.00003971876,0.00001554026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009756354,"about_ca_system_score_gemma":0.00009295324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001112385,"about_ca_topic_score_gemma":0.000006564932,"domain_scores_codex":[0.9991243,0.00001932555,0.0001113142,0.0003738758,0.0001574112,0.0002137594],"domain_scores_gemma":[0.999147,0.00001511696,0.00009930816,0.0005943725,0.00006939266,0.0000748062],"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":[0.0000213022,0.00002335402,0.00628989,0.000004352225,0.000003460518,0.000003392714,0.00001370973,0.0002125364,0.9862547,0.00031528,0.0000653416,0.006792692],"study_design_scores_gemma":[0.0006694432,0.0008270878,0.5187137,0.00003729599,0.00001627179,0.000005924065,0.00001682641,0.01586713,0.4114634,0.0002978111,0.05156897,0.0005161218],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929371,0.00001925585,0.005036893,0.0003366886,0.0002872771,0.0001215655,0.0001297176,0.000004150601,0.001127363],"genre_scores_gemma":[0.997951,0.00003392142,0.0009774612,0.0005589512,0.0000947884,0.00001141133,0.00004908729,0.000009050365,0.0003143017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5747913,"threshold_uncertainty_score":0.4054698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503517591284746,"score_gpt":0.2890163993418289,"score_spread":0.2639812234289814,"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."}}