{"id":"W3002189257","doi":"10.221751/rmc2016.045","title":"Dark Cutting Beef in the Canadian Grading System","year":2017,"lang":"en","type":"article","venue":"Meat and Muscle Biology","topic":"Meat and Animal Product Quality","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Marbled meat; Mathematics; Grading (engineering); Animal science; Agricultural science; Food science; Environmental science; Biology; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001429889,0.0004084802,0.0001986526,0.002110867,0.001397368,0.001270351,0.0008648183,0.0002179989,0.00536969],"category_scores_gemma":[0.001758998,0.0002351367,0.0002813983,0.002406938,0.0004079751,0.0002404273,0.0007535639,0.0003283485,0.0007536185],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008363613,"about_ca_system_score_gemma":0.008337718,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9202412,"about_ca_topic_score_gemma":0.9694416,"domain_scores_codex":[0.998439,0.0001186595,0.00006399681,0.0002268693,0.0008601948,0.0002912988],"domain_scores_gemma":[0.998136,0.00005184778,0.0002111184,0.00008447045,0.001285835,0.0002306247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001604823,0.0002153316,0.589267,0.000271687,0.0001235206,0.0003159304,0.001276829,0.001342125,0.03355367,0.002937162,0.02450551,0.3445864],"study_design_scores_gemma":[0.00002404976,0.0001334676,0.95676,0.00007116534,0.00005069871,0.0001537105,0.0005657597,0.001412619,0.001730084,0.0001677929,0.0388965,0.00003415293],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9188865,0.001662379,0.007620968,0.00039988,0.0001996085,0.0006629836,0.01109686,0.0002563413,0.05921453],"genre_scores_gemma":[0.933166,0.0009404317,0.02579541,0.0002854058,0.00002816064,0.0002060482,0.01072852,0.00008183862,0.0287683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9916364,"threshold_uncertainty_score":0.1604569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06179957850564347,"score_gpt":0.2685191766608477,"score_spread":0.2067195981552043,"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."}}