{"id":"W2761730640","doi":"","title":"Options for incorporating feed intake into national selection indexes","year":2017,"lang":"en","type":"article","venue":"Bulletin - International Bull Evaluation Service/Interbull bulletin","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Residual feed intake; Selection (genetic algorithm); Context (archaeology); Feed conversion ratio; Biotechnology; Biology; Statistics; Genomic selection; Production (economics); Index selection; Animal science; Mathematics; Body weight; Computer science; Genotype","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":[0.004975924,0.000601988,0.0006922777,0.0006528647,0.0003339021,0.001515748,0.00137244,0.0005580412,0.001910146],"category_scores_gemma":[0.01151809,0.0003995171,0.0007275306,0.001030374,0.0002878598,0.001282239,0.0008646045,0.0006609717,0.0002978309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028041,"about_ca_system_score_gemma":0.0007403395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005669472,"about_ca_topic_score_gemma":0.008883896,"domain_scores_codex":[0.9986728,0.0006894732,0.0001135018,0.0001945303,0.0002357812,0.00009382753],"domain_scores_gemma":[0.9962846,0.001939962,0.0003595232,0.0007621441,0.0005609835,0.00009283204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003002409,0.000259387,0.05141348,0.00008108852,0.0002927602,0.00007980616,0.0001939918,0.8442156,0.002661623,0.01395956,0.0007482099,0.08579414],"study_design_scores_gemma":[0.00006706132,0.0003120026,0.01589672,0.00005353902,0.0001112387,0.00005618444,0.0001016289,0.9622634,0.003304835,0.01245273,0.005310602,0.0000700974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.532949,0.0002948669,0.4436444,0.0004368943,0.00006433533,0.0002982095,0.001512616,0.001888229,0.0189115],"genre_scores_gemma":[0.8161278,0.0001395948,0.1807868,0.00009860333,0.00001349156,0.0002193299,0.001046935,0.0001291526,0.00143823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005669472,"threshold_uncertainty_score":0.02631557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03571606715714144,"score_gpt":0.3257332246659806,"score_spread":0.2900171575088392,"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."}}