{"id":"W2950143580","doi":"10.1093/bioinformatics/btz473","title":"DepthFinder: a tool to determine the optimal read depth for reduced-representation sequencing","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université Laval","funders":"Grain Farmers of Ontario; Canadian Field Crop Research Alliance; Genome Canada; Syngenta Canada; Government of Canada; Saskatchewan Pulse Growers","keywords":"Genotyping; DNA sequencing; Computational biology; Identification (biology); Biology; Genome; Selection (genetic algorithm); Computer science; Genetics; Machine learning; DNA; Genotype; Gene; Ecology","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":[0.006094006,0.002937603,0.001809287,0.004274832,0.00108044,0.002212049,0.003019311,0.001885911,0.009660002],"category_scores_gemma":[0.01712414,0.001481289,0.001493866,0.002630059,0.0006560655,0.002236596,0.001957882,0.002397374,0.004745092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233498,"about_ca_system_score_gemma":0.002328652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002559356,"about_ca_topic_score_gemma":0.003390256,"domain_scores_codex":[0.9972683,0.000614141,0.0002725541,0.0007251473,0.000936421,0.0001833596],"domain_scores_gemma":[0.9934376,0.004178884,0.001008971,0.0004248405,0.0007724222,0.0001772443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002675365,0.0005419641,0.03305775,0.004703211,0.0007133732,0.0007460688,0.00157716,0.07314649,0.189966,0.0147469,0.07422902,0.6038968],"study_design_scores_gemma":[0.000374208,0.0006592107,0.01147125,0.0004618905,0.0002264047,0.0007792403,0.0002442759,0.6348096,0.2615761,0.01560418,0.07324658,0.0005471635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02875385,0.001386931,0.8676237,0.0002891924,0.0001442002,0.0004627926,0.01088661,0.08841962,0.002033006],"genre_scores_gemma":[0.05739061,0.0003435859,0.9289684,0.0001924344,0.00003201956,0.0009151291,0.006430092,0.00461104,0.001116648],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009660002,"threshold_uncertainty_score":0.03231591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02702048160416613,"score_gpt":0.274243802773831,"score_spread":0.2472233211696649,"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."}}