{"id":"W4366827239","doi":"10.1139/cjas-2022-0132","title":"Modelling environmental impacts associated with the removal of productivity-enhancing technologies from Canadian feedlots: a case study","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Pharmacological Effects and Assays","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Agriculture Food and Rural Development; Agriculture and Agri-Food Canada; University of Manitoba; Canadian Science Centre for Human and Animal Health","funders":"Agriculture and Agri-Food Canada; Beef Cattle Research Council","keywords":"Feedlot; Animal science; Greenhouse gas; Productivity; Beef cattle; Environmental science; Ractopamine; Biology; Economics; Ecology","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":[0.000551097,0.0008773425,0.0006402666,0.0004602076,0.0009408762,0.001248765,0.001512163,0.0009898178,0.001520871],"category_scores_gemma":[0.0009174034,0.000390758,0.0009438211,0.001024691,0.0007231743,0.0003398282,0.0004525673,0.0005720941,0.00008678246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01445752,"about_ca_system_score_gemma":0.008153198,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9351051,"about_ca_topic_score_gemma":0.9445474,"domain_scores_codex":[0.999718,0.00005025708,0.000007787202,0.0000464088,0.00006052895,0.0001170528],"domain_scores_gemma":[0.9994022,0.0002872699,0.00005491238,0.00002399045,0.0001782641,0.00005346271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005045973,0.0001884112,0.02230584,0.0001599996,0.0001476055,0.0003923281,0.0000959343,0.9650195,0.004604734,0.0006042664,0.0005042255,0.00547258],"study_design_scores_gemma":[0.000167615,0.0007801702,0.06846484,0.00003029087,0.0002759595,0.00008367703,0.0009639974,0.9223182,0.003467711,0.0004213514,0.002940196,0.00008587255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947589,0.0001691046,0.001247421,0.00008839951,0.000007232735,0.00006721111,0.0008872451,0.00002695069,0.002747518],"genre_scores_gemma":[0.994265,0.0002798104,0.002296035,0.00002958651,0.000002313546,0.00004433686,0.0006220078,0.000009692022,0.002451126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06489491,"threshold_uncertainty_score":0.1305541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02719261120874334,"score_gpt":0.2299834200571719,"score_spread":0.2027908088484286,"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."}}