{"id":"W1844060830","doi":"10.2527/2000.7892282x","title":"Technical note: covariance adjustment in beef cattle research.","year":2000,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Covariate; Statistics; Breed; Analysis of covariance; Covariance; Mathematics; Beef cattle; Econometrics; Animal science; Biology","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":[0.07938537,0.002416243,0.001954495,0.003545521,0.001671688,0.002041754,0.004663203,0.002537378,0.02245775],"category_scores_gemma":[0.182069,0.001813499,0.002902333,0.005435948,0.004235212,0.003166135,0.004650581,0.01011732,0.01832486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643327,"about_ca_system_score_gemma":0.005827298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004103952,"about_ca_topic_score_gemma":0.005390278,"domain_scores_codex":[0.9406992,0.03873438,0.004403518,0.003361945,0.01197322,0.0008277911],"domain_scores_gemma":[0.8621996,0.07370208,0.006927359,0.02493943,0.03064242,0.001589127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005006635,0.0002296828,0.002530264,0.001876382,0.0003620567,0.000655485,0.0008421822,0.004310149,0.004125882,0.08321304,0.4340934,0.4672608],"study_design_scores_gemma":[0.0002339058,0.0005680167,0.006086457,0.001059818,0.000308756,0.001935899,0.000157449,0.01934173,0.006419329,0.09868506,0.864978,0.0002254516],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000504549,0.00193667,0.978084,0.002854397,0.009655569,0.0006501763,0.0008271047,0.001890814,0.003596636],"genre_scores_gemma":[0.009457114,0.002715432,0.9604321,0.003670985,0.005315856,0.004076234,0.001855628,0.002531211,0.009945539],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07938537,"threshold_uncertainty_score":0.419835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02826717020024563,"score_gpt":0.3351719285722141,"score_spread":0.3069047583719685,"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."}}