{"id":"W2031636859","doi":"10.2527/jas.2009-1801","title":"Bayesian analysis of selection for greater weaning weight while maintaining birth weight in beef cattle","year":2009,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western Forest Products; Canadian Forest Service; University of British Columbia","funders":"","keywords":"Beef cattle; Selection (genetic algorithm); Animal science; Weaning; Biology; Birth weight; Body weight; Pregnancy; Endocrinology; Genetics; Computer science; Machine learning","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":[0.00944441,0.0004196609,0.001074532,0.001115584,0.000561388,0.0008034869,0.0008748652,0.0006850143,0.001503565],"category_scores_gemma":[0.02163047,0.0004840294,0.001019743,0.0006379578,0.000657175,0.0005688711,0.000661572,0.0009077356,0.0001210162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009679667,"about_ca_system_score_gemma":0.0007284404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01458617,"about_ca_topic_score_gemma":0.01493962,"domain_scores_codex":[0.9979082,0.001431151,0.00007885314,0.000294754,0.0001490842,0.0001379578],"domain_scores_gemma":[0.9863093,0.01124941,0.001099533,0.0003779658,0.0005471685,0.0004165049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.003205887,0.0006152027,0.4257003,0.0003154953,0.002746663,0.0007442611,0.001175729,0.4507456,0.01784921,0.01720663,0.001937268,0.07775779],"study_design_scores_gemma":[0.0001667681,0.0002893995,0.2125729,0.0000469509,0.0002784982,0.000143275,0.0000907861,0.7786335,0.0005133514,0.006740715,0.0004522313,0.00007161691],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663799,0.0002832401,0.03248543,0.0001474409,0.000007837733,0.00002931638,0.0002161392,0.00007457825,0.0003761081],"genre_scores_gemma":[0.9917961,0.0000949594,0.007079142,0.00004759828,0.00001397369,0.00003100503,0.0005346145,0.00002491819,0.0003776912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01458617,"threshold_uncertainty_score":0.04994744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01109815874865028,"score_gpt":0.257587490207713,"score_spread":0.2464893314590628,"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."}}