{"id":"W1990210925","doi":"10.4141/a00-081","title":"Predicting loin-eye area from ultrasound and grading probe measurements of fat and muscle depths in pork carcasses","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Meat and Animal Product Quality","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Développement du Porc du Québec; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Loin; Perimeter; Ultrasound; Longissimus muscle; Eye muscle; Anatomy; Biomedical engineering; Mathematics; Medicine; Animal science; Biology; Radiology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0007109711,0.000361126,0.0002318075,0.0007039218,0.00005184482,0.0004494615,0.0002098525,0.0004793502,0.000500442],"category_scores_gemma":[0.002464596,0.0003212867,0.0002095116,0.0002537919,0.0001586253,0.0004056511,0.0002660448,0.0002151282,0.000250135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002168114,"about_ca_system_score_gemma":0.0001051468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002530975,"about_ca_topic_score_gemma":0.004255355,"domain_scores_codex":[0.9997696,0.00007185229,0.00001746825,0.00006750522,0.00004965395,0.00002379458],"domain_scores_gemma":[0.998887,0.0006893877,0.0002049743,0.00003979274,0.0001247748,0.00005415978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009549542,0.0000643827,0.8317254,0.0001048739,0.0001120272,0.0001709378,0.0001408805,0.01475097,0.1104766,0.00004780307,0.0001066651,0.04134447],"study_design_scores_gemma":[0.00001072696,0.0002782969,0.9329695,0.00001210691,0.00004596374,0.0001654471,0.0001016799,0.05746214,0.008746007,0.00006705534,0.000127512,0.00001370123],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934197,0.0001424731,0.006100174,0.000006443909,0.000001803708,0.000009954286,0.00008728904,0.00004472844,0.0001875147],"genre_scores_gemma":[0.9938903,0.0001040354,0.00553759,0.00001126497,0.000002889307,0.00001182859,0.0001841345,0.00001047796,0.0002476524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002530975,"threshold_uncertainty_score":0.005032539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08008464013419535,"score_gpt":0.2573430665405524,"score_spread":0.177258426406357,"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."}}