{"id":"W3117341588","doi":"10.3168/jds.2020-18241","title":"Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing","year":2020,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Guelph","funders":"Ontario Ministry of Research and Innovation; Genome Alberta; Beef Cattle Research Council; Agriculture and Agri-Food Canada; Ministry of Agriculture, Food and Rural Affairs; Beef Farmers of Ontario; Ontario Ministry of Agriculture, Food and Rural Affairs; Genome Canada; Ontario Genomics; Alberta Beef Producers","keywords":"Residual feed intake; Biology; Quantitative trait locus; Dairy cattle; Candidate gene; Genetics; Breed; Holstein Cattle; Gene; Population; Single-nucleotide polymorphism; SNP; Feed conversion ratio; Animal science; Genotype; Medicine","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":[0.0006016963,0.0001602331,0.0003103234,0.0006586017,0.000250973,0.0004338204,0.0002349332,0.000255909,0.0008071093],"category_scores_gemma":[0.0005750322,0.0001552776,0.0003830418,0.0004710585,0.0002982759,0.00009194382,0.0001683005,0.0002795159,0.0000905696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002075516,"about_ca_system_score_gemma":0.0001504529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003175827,"about_ca_topic_score_gemma":0.01231908,"domain_scores_codex":[0.999607,0.00006923043,0.00002269965,0.0001539514,0.00008553441,0.00006162711],"domain_scores_gemma":[0.9994696,0.0002939044,0.0001294771,0.00003472391,0.0000318314,0.00004046216],"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":[0.001328218,0.0000947247,0.3597935,0.0001140404,0.0005308894,0.0004590864,0.0006045605,0.0005560342,0.6168901,0.0002186265,0.0001274641,0.01928285],"study_design_scores_gemma":[0.00001552638,0.00007000333,0.9945156,0.000005610736,0.0001155406,0.0001470965,0.0000806596,0.000624712,0.004036115,0.00004011241,0.0003430977,0.000005929431],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983936,0.0001698904,0.0009555945,0.00001173718,0.000002959468,0.000005106767,0.0002533681,0.000006798495,0.0002009536],"genre_scores_gemma":[0.9950941,0.00009540719,0.002925084,0.00005096839,0.000008195178,0.00002384829,0.001410369,0.00001273127,0.0003793216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003175827,"threshold_uncertainty_score":0.006314695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02804969001097387,"score_gpt":0.2618505047882299,"score_spread":0.233800814777256,"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."}}