{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001732329,0.0001424435,0.0001523256,0.00003611284,0.00007406645,0.00003654786,0.0001761482,0.0001149732,0.000006660354],"category_scores_gemma":[0.00003953555,0.0001313507,0.00007647982,0.00005724456,0.00003122405,0.000003241349,0.00009043511,0.00005136897,0.00002056995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002795385,"about_ca_system_score_gemma":0.00008461609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000692437,"about_ca_topic_score_gemma":0.000001556148,"domain_scores_codex":[0.9991835,0.00001381899,0.0002803072,0.0001878722,0.0001001251,0.0002343505],"domain_scores_gemma":[0.9993489,0.00001730088,0.0001323822,0.0003464929,0.0001029824,0.0000519122],"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":[0.00005633618,0.00002414155,0.001060416,0.0001816351,0.00008431616,9.723443e-8,0.00046779,0.0008716403,0.9945712,0.0004398233,0.0003816962,0.001860907],"study_design_scores_gemma":[0.007260098,0.002949716,0.06559598,0.00009748809,0.0002888042,0.0001348194,0.01625883,0.06720473,0.607395,0.001625956,0.2277286,0.003460003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971576,0.0003203901,0.01334407,0.00001333319,0.0001480383,0.0006538967,0.00008137496,0.000007632154,0.0138553],"genre_scores_gemma":[0.9422772,0.0001373001,0.05647669,0.0001032231,0.0001159331,0.00005120203,0.0003683823,0.00001643323,0.0004536706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3871762,"threshold_uncertainty_score":0.5356325,"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."}}