{"id":"W4378596986","doi":"10.1071/an23022","title":"Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle","year":2023,"lang":"en","type":"article","venue":"Animal Production Science","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; Valacta (Canada); Ste. Anne's Hospital","funders":"Ontario Ministry of Research and Innovation; Ministry of Agriculture, Food and Rural Affairs; Agriculture Victoria; Dairy Australia; Gardiner Foundation; Ontario Ministry of Agriculture, Food and Rural Affairs; Agricultural Research Service; Genome Canada; Ontario Genomics; Aarhus Universitet; Genome Alberta; U.S. Department of Agriculture","keywords":"Residual feed intake; Dry matter; Lactation; Environmental management system; Animal science; Residual; Dairy cattle; Environmental science; Water intake; Agronomy; Irrigation; Biology; Mathematics; Body weight; Feed conversion ratio; Pregnancy; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002082778,0.0002974751,0.0006329936,0.0007423133,0.0003654007,0.00100534,0.0003822484,0.0004297089,0.001149528],"category_scores_gemma":[0.004600499,0.0002633302,0.0005517167,0.00133998,0.0002800569,0.0003079823,0.0004871612,0.0004136387,0.000303396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002900375,"about_ca_system_score_gemma":0.0002094815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005277593,"about_ca_topic_score_gemma":0.01394409,"domain_scores_codex":[0.9988416,0.0004564922,0.00009072118,0.0004034604,0.0001468424,0.00006098579],"domain_scores_gemma":[0.996004,0.001879459,0.001173341,0.0005345604,0.0002721084,0.0001364605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007867536,0.00006349052,0.9459115,0.0001651813,0.0009307291,0.0001229712,0.0001721807,0.002444985,0.01563116,0.0001061006,0.0002961078,0.03336874],"study_design_scores_gemma":[0.0000112113,0.0001151465,0.9933404,0.00002268531,0.0001783722,0.0001502787,0.00004346971,0.004105915,0.00124372,0.0001717758,0.0006059672,0.00001106851],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898956,0.0008593601,0.007075027,0.00007012892,0.00001596447,0.00001009488,0.001296072,0.00007408461,0.0007036907],"genre_scores_gemma":[0.9894336,0.0002539051,0.008394654,0.00007492105,0.0000167972,0.00001761703,0.001404943,0.00001887196,0.0003847302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005277593,"threshold_uncertainty_score":0.01101494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02390831759344251,"score_gpt":0.2582617783689611,"score_spread":0.2343534607755186,"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."}}