{"id":"W2992734264","doi":"10.1093/jas/skz258.551","title":"PSVIII-19 Meta-analysis of genetic parameter estimates for feed efficiency traits in dairy cattle","year":2019,"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 Guelph","funders":"","keywords":"Residual feed intake; Heritability; Selection (genetic algorithm); Biology; Dairy cattle; Animal science; Biotechnology; Feed conversion ratio; Statistics; Genetic gain; Dry matter; Profitability index; Meta-analysis; Mathematics; Genetic variation; Body weight; Computer science; Economics; Medicine","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":[0.02345965,0.003443693,0.01063433,0.007849885,0.001048799,0.004484059,0.002565045,0.00253417,0.006164822],"category_scores_gemma":[0.04412721,0.001302549,0.04617621,0.008736675,0.0008857386,0.001558494,0.001721271,0.002848654,0.000625184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001746827,"about_ca_system_score_gemma":0.002213967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007255644,"about_ca_topic_score_gemma":0.007068485,"domain_scores_codex":[0.9776565,0.01337255,0.003399732,0.003493248,0.001565992,0.0005118595],"domain_scores_gemma":[0.9656968,0.02767689,0.002548114,0.002089948,0.001643196,0.0003450694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.001515496,0.00002771216,0.02711308,0.02138402,0.9381407,0.0001980001,0.00005647148,0.001886522,0.0005326797,0.0002242245,0.0009523495,0.007968725],"study_design_scores_gemma":[0.0003538429,0.0001981691,0.01990157,0.002499962,0.9723962,0.0001808029,0.00004220954,0.001437365,0.0003113876,0.0007067058,0.001936814,0.00003495298],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.06774521,0.9017623,0.01651588,0.001501521,0.001012074,0.0003317405,0.009422974,0.000466167,0.001242195],"genre_scores_gemma":[0.8764176,0.1022762,0.01075387,0.001303228,0.0006942729,0.0009171012,0.006443933,0.0002814954,0.0009124131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02345965,"threshold_uncertainty_score":0.124068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03330462767557731,"score_gpt":0.3010790384783713,"score_spread":0.267774410802794,"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."}}