{"id":"W2570111040","doi":"10.1101/092940","title":"SNVPhyl: A Single Nucleotide Variant Phylogenomics pipeline for microbial genomic epidemiology","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control; University of British Columbia; Dalhousie University; Simon Fraser University; Health Canada; University of Manitoba; Public Health Agency of Canada","funders":"Genome British Columbia; Genome Canada","keywords":"Phylogenomics; Scalability; Computational biology; Genome; Whole genome sequencing; Workflow; Indel; Biology; Pipeline (software); Computer science; Data mining; Genetics; Phylogenetic tree; Single-nucleotide polymorphism; Database; Clade; Gene; Operating system","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":[0.00104124,0.0008735884,0.001058213,0.0001537656,0.0002492283,0.00006680127,0.0008981539,0.0009927175,0.00001127267],"category_scores_gemma":[0.0005513373,0.0008551745,0.000539137,0.0000937321,0.000296419,0.000002085863,0.001269333,0.0003289776,0.00003607084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001895086,"about_ca_system_score_gemma":0.0005634638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002425882,"about_ca_topic_score_gemma":0.000009442385,"domain_scores_codex":[0.9957379,0.000225415,0.00102743,0.001804996,0.0001088069,0.001095478],"domain_scores_gemma":[0.9965898,0.0001496654,0.0007699761,0.001671009,0.0005169231,0.0003025667],"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":[0.000191878,0.0001514744,0.001608365,0.0001692253,0.0004446729,0.000008327625,0.000005074181,0.00008639491,0.9939286,0.0003206489,0.003070769,0.00001458392],"study_design_scores_gemma":[0.002072578,0.0004150856,0.01295944,0.0001582155,0.0003472694,2.723856e-7,0.000002938805,0.0002004633,0.7690161,0.0001167167,0.2127688,0.001942149],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.955704,0.007192885,0.02851723,0.0006880533,0.003317376,0.001571073,0.002928183,0.00005523778,0.00002596268],"genre_scores_gemma":[0.9753938,0.001393837,0.01715502,0.0009973909,0.004385148,0.0003484883,0.000005960339,0.0002840158,0.0000363324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2249125,"threshold_uncertainty_score":0.9993899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02172959438046663,"score_gpt":0.232315305868616,"score_spread":0.2105857114881494,"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."}}