{"id":"W2895617841","doi":"10.1139/cjas-2018-0078","title":"Quantitative study of genetic gain for growth, carcass, and morphological traits of Nelore cattle","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Rump; Heritability; Beef cattle; Animal science; Biology; Genetic gain; Genetic correlation; Selection (genetic algorithm); Loin; Population; Genetic variation; Genetics; Medicine; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009732525,0.0002808979,0.0002363649,0.0006690007,0.0002179634,0.0002855699,0.0001845743,0.0001957983,0.001140489],"category_scores_gemma":[0.001028969,0.0001312892,0.0001941491,0.0005159869,0.0002764355,0.0001683385,0.0001818138,0.0002343201,0.0001263033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002519354,"about_ca_system_score_gemma":0.0001395677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002560177,"about_ca_topic_score_gemma":0.005293811,"domain_scores_codex":[0.9995294,0.0001868772,0.00002256239,0.0001272756,0.0001003167,0.00003366904],"domain_scores_gemma":[0.9994349,0.0002324132,0.0001525861,0.00004874592,0.00005347732,0.00007784378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00144852,0.0003043624,0.8405749,0.00005067098,0.000407456,0.0006853872,0.001031445,0.001159045,0.1375548,0.000259059,0.00006480263,0.01645954],"study_design_scores_gemma":[0.000008247565,0.000141222,0.9978353,0.000003438861,0.00002380918,0.0002750813,0.0001164738,0.0007746024,0.000624484,0.00002754096,0.0001653321,0.000004499532],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99959,0.00003394482,0.0002330175,0.000004228727,4.906319e-7,0.000001653356,0.00003852152,0.000001397014,0.00009673124],"genre_scores_gemma":[0.9988545,0.00003677556,0.0005931444,0.000004227453,0.000002541599,0.000005774035,0.0001604475,0.000003253763,0.0003393968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002560177,"threshold_uncertainty_score":0.005147099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03159667400810115,"score_gpt":0.2848391616391365,"score_spread":0.2532424876310354,"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."}}