{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009358717,0.001032828,0.001064833,0.0005819629,0.0005529705,0.0007393806,0.003117928,0.001497922,0.005824854],"category_scores_gemma":[0.004183944,0.0006881203,0.001189015,0.0007446879,0.0005468566,0.001089357,0.000953987,0.001747954,0.001367377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089873,"about_ca_system_score_gemma":0.002086871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009932742,"about_ca_topic_score_gemma":0.006720923,"domain_scores_codex":[0.9994611,0.000126146,0.00003684713,0.00009179147,0.0002242311,0.00005992275],"domain_scores_gemma":[0.9981338,0.001087206,0.0001423807,0.0001736193,0.0003449236,0.0001179994],"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":[0.0001381787,0.0000872184,0.002146866,0.0002717657,0.00009671594,0.0001208738,0.0001145956,0.9647626,0.006529747,0.009789241,0.007199197,0.008742929],"study_design_scores_gemma":[0.00001975735,0.00001980196,0.0001162294,0.000007453468,0.000006478978,0.00001460817,0.000007938664,0.9928913,0.002453998,0.002022663,0.002426897,0.00001278062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1101808,0.001025903,0.8091733,0.0009969592,0.0003509264,0.000457197,0.01368776,0.04767268,0.01645445],"genre_scores_gemma":[0.5954147,0.0009483232,0.3644242,0.0008120711,0.0001246743,0.0018752,0.01821021,0.01075615,0.007434376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009932742,"threshold_uncertainty_score":0.01974988,"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."}}