{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000136789,0.000130354,0.0001067348,0.00002616313,0.0000841451,0.00004437108,0.0002413228,0.00009357621,0.00001760333],"category_scores_gemma":[0.0001022698,0.00009738036,0.00007590724,0.00006909933,0.0000286072,0.000008591369,0.00008751239,0.00005652606,0.00005215393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001694312,"about_ca_system_score_gemma":0.0001066702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005739355,"about_ca_topic_score_gemma":0.00001104428,"domain_scores_codex":[0.9992043,0.00001397259,0.0002725978,0.0001525315,0.0001221243,0.0002344215],"domain_scores_gemma":[0.9993303,0.00003197467,0.0000901308,0.0004154638,0.00007571081,0.00005641171],"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":[0.0008714511,0.0001250021,0.004590171,0.0004953826,0.0003714054,6.585017e-7,0.008287155,0.07024646,0.3724293,0.007258818,0.03118641,0.5041378],"study_design_scores_gemma":[0.006453775,0.006862527,0.09254166,0.0002136988,0.0003089623,0.0002599124,0.01243322,0.05666829,0.6121372,0.00217517,0.207212,0.00273356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.873236,0.00003654751,0.1220412,0.0001368164,0.0002717756,0.0008808103,0.00002260104,0.00001463342,0.00335963],"genre_scores_gemma":[0.6923355,0.00000512158,0.3049285,0.0007276839,0.000199721,0.00007845909,0.00009491274,0.0000179997,0.001612133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5014042,"threshold_uncertainty_score":0.3971056,"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."}}