{"id":"W2769946796","doi":"10.1016/j.anifeedsci.2017.11.013","title":"Effects of feeding canola meal or soy expeller at two dietary net energy levels on growth performance, dressing and carcass characteristics of barrows and gilts","year":2017,"lang":"en","type":"article","venue":"Animal Feed Science and Technology","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Ministry of Agriculture and Forestry","funders":"Agriculture and Agri-Food Canada; Canola Council of Canada","keywords":"Canola; Meal; Animal science; Net energy; Soybean meal; Feed conversion ratio; Chemistry; Factorial experiment; Energy density; Food science; Body weight; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005206592,0.001187502,0.0009479523,0.0006548442,0.0008091027,0.0008324189,0.0005998859,0.001211902,0.002757447],"category_scores_gemma":[0.001509893,0.0008720588,0.0006263279,0.000402416,0.001819357,0.001059656,0.0005643339,0.001927575,0.0004313675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009952224,"about_ca_system_score_gemma":0.0008517649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00802945,"about_ca_topic_score_gemma":0.01176087,"domain_scores_codex":[0.9995222,0.0001074232,0.00003527205,0.0001229488,0.0000378328,0.0001742243],"domain_scores_gemma":[0.998158,0.0005709023,0.0001570116,0.00008346135,0.0001087952,0.0009218277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.5778973,0.01206582,0.007666072,0.0003279588,0.0003361194,0.0004884157,0.0008459029,0.0005677561,0.3902149,0.0002171715,0.0003459799,0.009026644],"study_design_scores_gemma":[0.007312275,0.2426939,0.230401,0.000154309,0.00209352,0.0004738678,0.002744211,0.005758855,0.5046301,0.0007726083,0.002615202,0.0003502377],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993933,0.0001129561,0.00007249981,0.00005165501,0.00002784302,0.00001258632,0.0001196758,0.000008900971,0.0002005044],"genre_scores_gemma":[0.994426,0.0002502307,0.0004790057,0.0002198107,0.00005025146,0.00006540202,0.0003722373,0.00002909896,0.004107881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00802945,"threshold_uncertainty_score":0.01596546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02567293779828748,"score_gpt":0.2439280925502502,"score_spread":0.2182551547519627,"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."}}