{"id":"W2429273004","doi":"","title":"Genomic prediction of beef tenderness in Canadian beef cattle","year":2014,"lang":"en","type":"article","venue":"Figshare","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tenderness; Beef cattle; Animal science; Biology; Food science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000749724,0.0003676919,0.0002610877,0.001140662,0.0009857904,0.000599968,0.0004832805,0.0002982074,0.002103895],"category_scores_gemma":[0.001400034,0.0001587931,0.0004495067,0.001426465,0.0004155419,0.0001152641,0.0003780786,0.0004361371,0.0002864212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006655933,"about_ca_system_score_gemma":0.003356108,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9293941,"about_ca_topic_score_gemma":0.9568419,"domain_scores_codex":[0.9996629,0.00003986571,0.000008028452,0.0001056089,0.0001079479,0.00007574603],"domain_scores_gemma":[0.9995261,0.0001296311,0.00004420244,0.00002789978,0.0002109792,0.00006116474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001036609,0.0001178259,0.8704689,0.0000622794,0.0003895523,0.0003477488,0.001125842,0.01057242,0.03355166,0.001067911,0.003080634,0.07817855],"study_design_scores_gemma":[0.00001364769,0.00003254916,0.990573,0.000009904703,0.00005007504,0.00006152442,0.0002107275,0.005915734,0.0009090853,0.0001139576,0.002091925,0.00001783896],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926822,0.0002049628,0.00186192,0.0001140714,0.000004704199,0.00001623742,0.003322314,0.00003915019,0.001754495],"genre_scores_gemma":[0.9872191,0.0001873287,0.00432403,0.00005638086,0.000003842507,0.00001299168,0.006138457,0.00002631277,0.002031501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07060587,"threshold_uncertainty_score":0.1420433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506495756521733,"score_gpt":0.2189649290533852,"score_spread":0.2038999714881679,"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."}}