{"id":"W2801636125","doi":"10.1139/cjas-2017-0208","title":"Exploration of methods for lamb carcass yield estimation in Canada","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Meat and Animal Product Quality","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Crop Industry Development Fund; Agriculture and Agri-Food Canada","funders":"Alberta Livestock and Meat Agency; University of Connecticut","keywords":"Lean meat; Carcass weight; Yield (engineering); Mathematics; Animal science; Regression analysis; Population; Linear regression; Lean tissue; Stepwise regression; Statistics; Biology; Body weight; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.004078754,0.0009514943,0.0006959696,0.003843398,0.001250411,0.00190881,0.001612198,0.0003693216,0.003028204],"category_scores_gemma":[0.00986903,0.0004856715,0.0007011085,0.003544229,0.0003804405,0.0006056196,0.001045837,0.0005536809,0.0006073354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01282284,"about_ca_system_score_gemma":0.01519207,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9218293,"about_ca_topic_score_gemma":0.9321777,"domain_scores_codex":[0.9976138,0.0004946494,0.0001292205,0.000387466,0.001215093,0.0001597457],"domain_scores_gemma":[0.9952645,0.0008829111,0.0003019409,0.0001742757,0.003274182,0.0001021474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003720955,0.0001149859,0.1180692,0.0005871723,0.0003297914,0.0002123773,0.0008776213,0.05268832,0.0159284,0.004754719,0.004920526,0.8011448],"study_design_scores_gemma":[0.0001028703,0.000213362,0.2710296,0.0002789499,0.0002991189,0.0003450352,0.001592137,0.6773773,0.01764645,0.003102397,0.02781763,0.0001951703],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2570889,0.003438223,0.7071921,0.0006730074,0.00009151517,0.001076601,0.00482499,0.006333732,0.01928097],"genre_scores_gemma":[0.431104,0.001385655,0.553973,0.0001322567,0.00002109565,0.0003920944,0.002291188,0.0004591307,0.01024159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07817072,"threshold_uncertainty_score":0.1572621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1534950416923896,"score_gpt":0.3435620521746381,"score_spread":0.1900670104822485,"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."}}