{"id":"W2911619868","doi":"10.1093/bioinformatics/btz043","title":"SRG extractor: a skinny reference genome approach for reduced-representation sequencing","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université Laval","funders":"Génome Québec; Iowa State University; Genome Canada","keywords":"Reference genome; Genome; Genotyping; Extractor; Computational biology; Computer science; DNA sequencing; Pipeline (software); Biology; Genetics; Gene; Genotype; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002630849,0.002779472,0.00155388,0.003577741,0.001239083,0.002368186,0.003394561,0.001844702,0.03752571],"category_scores_gemma":[0.01284898,0.001580679,0.001701681,0.003759614,0.0005902931,0.002169593,0.003091287,0.002716052,0.03748483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007816107,"about_ca_system_score_gemma":0.001809781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002749758,"about_ca_topic_score_gemma":0.00473301,"domain_scores_codex":[0.9981002,0.0002914155,0.000206461,0.0006961515,0.00056215,0.0001436236],"domain_scores_gemma":[0.9966029,0.001175283,0.0003747418,0.0008996556,0.0008093137,0.0001380931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002203136,0.0002373008,0.005848081,0.004814374,0.0006846348,0.001171891,0.001059146,0.008265356,0.1244232,0.01796626,0.5877514,0.2455752],"study_design_scores_gemma":[0.0004166215,0.0002402328,0.007523281,0.0007122956,0.0002821818,0.001145972,0.0002825674,0.04114172,0.1318049,0.02880931,0.7872941,0.0003468364],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006249705,0.0007731555,0.564062,0.0005748191,0.0004718358,0.0003846713,0.1221029,0.2989256,0.006455276],"genre_scores_gemma":[0.01888773,0.0006280862,0.6404223,0.0007827845,0.0001233479,0.0009638809,0.2677793,0.06352097,0.006891602],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03752571,"threshold_uncertainty_score":0.125536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03827263071399067,"score_gpt":0.2684018089181237,"score_spread":0.230129178204133,"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."}}