{"id":"W4388539474","doi":"10.1093/jas/skad281.158","title":"212 Genome-Wide Association Study Investigating the Genomic Components of Efficiency in Beef Cows","year":2023,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Residual feed intake; Genome-wide association study; Beef cattle; Biology; Genomic selection; Ice calving; Restricted maximum likelihood; Percentile; Best linear unbiased prediction; Animal science; Statistics; Single-nucleotide polymorphism; Genetics; Mathematics; Feed conversion ratio; Body weight; Computer science; Maximum likelihood; Genotype; Lactation; Pregnancy; Selection (genetic algorithm); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001738949,0.0002577505,0.0004686657,0.0006580778,0.0003807772,0.0006130736,0.0003180832,0.0003768534,0.001906826],"category_scores_gemma":[0.001689027,0.000202585,0.0009503469,0.001357143,0.0001859694,0.0001701968,0.0003087663,0.0004156786,0.0001241489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001750362,"about_ca_system_score_gemma":0.0001985888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003921076,"about_ca_topic_score_gemma":0.004632378,"domain_scores_codex":[0.9990447,0.0003903134,0.00007271414,0.0003188143,0.00008714868,0.00008618834],"domain_scores_gemma":[0.9984376,0.0007791338,0.0003771709,0.0001725701,0.000110131,0.0001234241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008169225,0.00005043838,0.9898174,0.00003477005,0.001140284,0.0001119156,0.00006304924,0.0002960554,0.003963443,0.00005335444,0.0001639636,0.003488369],"study_design_scores_gemma":[0.000022504,0.0001189282,0.997833,0.000005548891,0.0002936244,0.00006825895,0.0000366805,0.001261862,0.0001485013,0.00003614055,0.0001709657,0.000004069804],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984905,0.0003550434,0.0005683478,0.00002845729,0.000006755514,0.000003564573,0.0004449441,0.000008693863,0.00009373026],"genre_scores_gemma":[0.9985721,0.00006731996,0.0006391204,0.00002504597,0.0000114149,0.000007869409,0.0005561021,0.000005187743,0.0001156794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003921076,"threshold_uncertainty_score":0.009196579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02310367132735458,"score_gpt":0.2716213439765189,"score_spread":0.2485176726491643,"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."}}