{"id":"W2342023238","doi":"10.1101/044545","title":"NanoSim: nanopore sequence read simulator based on statistical characterization","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Nanopore and Nanochannel Transport Studies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia; BC Cancer Agency","funders":"National Institutes of Health; Genome British Columbia; Genome Canada","keywords":"Nanopore; Sequence (biology); Characterization (materials science); Computer science; Time sequence; Nanopore sequencing; Simulation; Algorithm; Nanotechnology; Materials science; Chemistry","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":[0.0002883607,0.0008782369,0.0008050965,0.0003482563,0.0001835618,0.0001029812,0.0004663274,0.0006712397,0.0001501711],"category_scores_gemma":[0.00009995326,0.0008167626,0.0001557557,0.000369293,0.0001505288,0.0001534628,0.0001045947,0.0006115275,0.000156393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003632651,"about_ca_system_score_gemma":0.0002907141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005601183,"about_ca_topic_score_gemma":8.852675e-7,"domain_scores_codex":[0.9968911,0.00006671974,0.0007090499,0.000985548,0.0005694454,0.0007780757],"domain_scores_gemma":[0.9978426,0.0001419623,0.0002112747,0.001161176,0.000336828,0.000306202],"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":[0.00007059261,0.0001212143,0.002309268,0.00104748,0.0002031936,0.000225183,0.00001070728,0.002703834,0.991943,0.0008596038,0.0004919104,0.00001402749],"study_design_scores_gemma":[0.001720143,0.0002481873,0.05530685,0.003363322,0.0003505331,2.247034e-8,0.000001404395,0.0343069,0.8742055,0.00002053502,0.02705881,0.00341779],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7799998,0.0008330275,0.1876309,0.000645959,0.008704672,0.002773855,0.0139342,0.00534089,0.000136778],"genre_scores_gemma":[0.9970748,0.0003758299,0.001203048,0.0002346967,0.000559803,0.0002535147,0.00001068697,0.0002764008,0.00001122999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.217075,"threshold_uncertainty_score":0.9994283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471491851048277,"score_gpt":0.2145114039771089,"score_spread":0.1997964854666262,"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."}}