{"id":"W3169935286","doi":"10.3389/fgene.2021.665344","title":"Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations","year":2021,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Dalhousie University","funders":"","keywords":"Best linear unbiased prediction; Loin; Biology; Single-nucleotide polymorphism; Imputation (statistics); Animal science; Genomic selection; Large white; Biotechnology; Statistics; Genetics; Mathematics; Selection (genetic algorithm); Computer science; Genotype; Gene; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001150908,0.0004301142,0.000286365,0.0007164386,0.0006158624,0.0006388108,0.0004417767,0.0002872353,0.0006170989],"category_scores_gemma":[0.001566289,0.0001610189,0.0004804469,0.001095394,0.0003479457,0.0001823339,0.0003392711,0.0003177298,0.0001476061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002723517,"about_ca_system_score_gemma":0.002481342,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6937965,"about_ca_topic_score_gemma":0.8442248,"domain_scores_codex":[0.9994465,0.00008323867,0.0000168103,0.000200016,0.0001661565,0.0000872958],"domain_scores_gemma":[0.9993799,0.0001764383,0.00009438198,0.00005495291,0.0002473088,0.00004701837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003903234,0.00003511896,0.9218547,0.00006287838,0.0004040782,0.0001725411,0.000483349,0.00860926,0.01494068,0.0004009973,0.0006496311,0.05199647],"study_design_scores_gemma":[0.00001272464,0.00003908334,0.9810526,0.00002339114,0.0001350202,0.00007840691,0.000227572,0.01507383,0.001736399,0.000119537,0.001476304,0.00002508675],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917393,0.0004512716,0.005324489,0.00006247928,0.00000544524,0.00001148827,0.001318989,0.00004384457,0.001042613],"genre_scores_gemma":[0.9887978,0.0002942923,0.007218764,0.00004549356,0.000003842184,0.00001565132,0.002726972,0.00002339149,0.000873643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3062035,"threshold_uncertainty_score":0.6160136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02359009406632765,"score_gpt":0.2331251548220466,"score_spread":0.209535060755719,"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."}}