{"id":"W2140213234","doi":"10.1111/jbg.12036","title":"Linear and <scp>P</scp>oisson models for genetic evaluation of tick resistance in cross‐bred <scp>H</scp>ereford x <scp>N</scp>ellore cattle","year":2013,"lang":"en","type":"article","venue":"Journal of Animal Breeding and Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Goodness of fit; Statistics; Generalized linear model; Deviance (statistics); Heritability; Linear model; Mathematics; Poisson distribution; Residual; Biology; Genetic model; Random effects model; Tick; Trait; Genetics; Ecology; Computer science; Gene","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"],"consensus_categories":[],"category_scores_codex":[0.0008497614,0.0003376997,0.0004699857,0.0001571553,0.0001340453,0.00009428227,0.0003150842,0.0003815286,0.000002254123],"category_scores_gemma":[0.001057258,0.0003150884,0.0001471447,0.0001564572,0.0002534964,0.00003002056,0.0001268595,0.0002568235,0.000001497474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003337399,"about_ca_system_score_gemma":0.000205614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001795907,"about_ca_topic_score_gemma":0.00003649772,"domain_scores_codex":[0.9975431,0.0001044182,0.0008821954,0.0004513899,0.0005130298,0.0005058444],"domain_scores_gemma":[0.9975159,0.0003301132,0.0006315044,0.0002649636,0.0009997019,0.0002578338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002487747,0.00067932,0.07203759,0.001027371,0.0006561331,0.000005447672,0.008606059,0.05552799,0.8272771,0.0009578736,0.02258787,0.01038851],"study_design_scores_gemma":[0.007452367,0.008521006,0.801333,0.0004236749,0.0006675859,0.0001978149,0.00435601,0.02431282,0.1168891,0.02401258,0.01156956,0.0002644642],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9778888,0.01635581,0.004099145,0.00004746835,0.0002360306,0.0005082217,0.00004492548,0.000005944865,0.0008136768],"genre_scores_gemma":[0.943854,0.001112811,0.05349391,0.00006729726,0.0006060227,0.00002220618,0.00001618197,0.00005227579,0.0007753051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7292954,"threshold_uncertainty_score":0.9999301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03383024684655733,"score_gpt":0.2855123475243403,"score_spread":0.251682100677783,"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."}}