{"id":"W7056898358","doi":"","title":"Genomics of beef tenderness in Canadian beef cattle","year":2015,"lang":"en","type":"article","venue":"Figshare","topic":"Particle accelerators and beam dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tenderness; Beef cattle; Beef industry; Population; Longissimus dorsi; Genomics; Selection (genetic algorithm)","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":[0.0003639686,0.000258306,0.0002909592,0.001186795,0.001261541,0.0006483158,0.0003584313,0.0003176738,0.001876313],"category_scores_gemma":[0.0005825007,0.0001624175,0.0003711596,0.001856256,0.0004092129,0.0001130499,0.0003567306,0.0004239886,0.0002136373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007944869,"about_ca_system_score_gemma":0.003634215,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9274971,"about_ca_topic_score_gemma":0.9576701,"domain_scores_codex":[0.9996603,0.000021349,0.00000571793,0.00009872027,0.0001270716,0.00008696485],"domain_scores_gemma":[0.9997117,0.00004627628,0.00003611369,0.00001265828,0.0001383685,0.00005483157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001568294,0.0002050866,0.6153123,0.0002098306,0.0004366241,0.0006576261,0.005498623,0.00444513,0.2660788,0.002405554,0.005131892,0.09805028],"study_design_scores_gemma":[0.000006443346,0.00003390747,0.9949152,0.000009113416,0.0000309272,0.00006469888,0.0003548954,0.000826116,0.000871401,0.00007408106,0.002801928,0.00001138478],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990214,0.0004399729,0.0009900255,0.0001775919,0.000005829336,0.00002198435,0.00512519,0.00002733084,0.002998151],"genre_scores_gemma":[0.9858716,0.0004322124,0.002743984,0.00015982,0.000005355595,0.00002381453,0.006676258,0.00003674998,0.004050197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07250285,"threshold_uncertainty_score":0.1458596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03895548857551909,"score_gpt":0.2354639535348966,"score_spread":0.1965084649593775,"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."}}