{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001949257,0.0004393122,0.0002898266,0.0001759104,0.001039751,0.0004424389,0.001283113,0.0003599559,0.003179574],"category_scores_gemma":[0.001776881,0.0004824399,0.0002367641,0.00007431402,0.0001765841,0.00001857195,0.0004573811,0.0002616904,0.0006306299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000204934,"about_ca_system_score_gemma":0.0003425585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003303757,"about_ca_topic_score_gemma":0.0003914295,"domain_scores_codex":[0.9965698,0.0002070665,0.0007984782,0.0009687855,0.001065866,0.0003900735],"domain_scores_gemma":[0.9950747,0.0001105304,0.0008626857,0.0006021896,0.003184995,0.0001649335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003676359,0.001701155,0.01265713,0.0003501029,0.002038297,0.000002202228,0.001041039,0.06101403,0.1017313,0.1002587,0.6902558,0.02527385],"study_design_scores_gemma":[0.00453091,0.0006534138,0.03222783,0.0001632259,0.0001576364,0.00005971964,0.0003157851,0.00637303,0.008538294,0.01691085,0.929122,0.0009472452],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4713409,0.0009915712,0.2176998,0.2081426,0.007947687,0.00739243,0.0008203617,0.0003969723,0.08526766],"genre_scores_gemma":[0.8958506,0.00002232779,0.09109653,0.003672437,0.002150092,0.0009245072,0.001511475,0.00008685191,0.004685192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4245097,"threshold_uncertainty_score":0.9997627,"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."}}