{"id":"W2012606811","doi":"10.4141/a03-009","title":"Evaluation of the National Research Council (NRC) nutrient requirements for beef cattle: Predicting feedlot performance","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feedlot; Dry matter; Net energy; Silage; Animal science; Beef cattle; Neutral Detergent Fiber; Nutrient; Mathematics; Biotechnology; Linear regression; Biology; Statistics","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":[0.002384602,0.0004425995,0.0003179529,0.0003597128,0.0001751016,0.0004268263,0.0002756463,0.0003031967,0.0003600147],"category_scores_gemma":[0.003230855,0.0001651109,0.0001828059,0.0002783583,0.0001023294,0.0001899877,0.0001235459,0.0002122469,0.0001675944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006390754,"about_ca_system_score_gemma":0.0005838071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02788236,"about_ca_topic_score_gemma":0.0506312,"domain_scores_codex":[0.9994106,0.0002195888,0.00004714042,0.0001025813,0.0001881216,0.00003190843],"domain_scores_gemma":[0.9980148,0.001143604,0.0002673534,0.0000584297,0.0004089889,0.000106786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002057065,0.0003063603,0.9065682,0.0001001853,0.0002737335,0.0000918536,0.0001050845,0.02285287,0.02240166,0.00004518354,0.0004626276,0.04473525],"study_design_scores_gemma":[0.0000755888,0.001383032,0.7954556,0.00002142533,0.0001297739,0.0001223993,0.0001138677,0.1955279,0.006460791,0.00004374719,0.0006481767,0.00001765957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970874,0.00009391159,0.002219344,0.00001655504,0.000003633501,0.000007760159,0.000218188,0.00003687338,0.000316237],"genre_scores_gemma":[0.9952669,0.0000477165,0.00375632,0.00001179579,0.000002857676,0.000009907632,0.0005774941,0.000006041646,0.0003209921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02788236,"threshold_uncertainty_score":0.05544013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2943172272532428,"score_gpt":0.3515542129833573,"score_spread":0.05723698573011454,"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."}}