{"id":"W2782777865","doi":"10.1017/aae.2019.41","title":"Greenhouse Gas Emissions and Technical Efficiency in Alberta Dairy Production: What Are the Trade-Offs?","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural and Applied Economics","topic":"Agriculture Sustainability and Environmental Impact","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Livestock and Meat Agency; Canadian Dairy Commission; Alberta Agriculture and Forestry","keywords":"Greenhouse gas; Production (economics); Tonne; Agricultural economics; Revenue; Natural resource economics; Environmental science; Economics; Environmental economics; Agricultural science; Business; Waste management; Engineering; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002336327,0.0003880171,0.0002824711,0.00117526,0.0004154522,0.002782678,0.0005857786,0.0005491276,0.001683751],"category_scores_gemma":[0.004188632,0.0001666224,0.0005025601,0.002047288,0.001318865,0.001261718,0.0006558666,0.0005666253,0.0001241691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0122251,"about_ca_system_score_gemma":0.005354624,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5259746,"about_ca_topic_score_gemma":0.6874888,"domain_scores_codex":[0.9982365,0.0003559544,0.00005052798,0.0001605019,0.0008352282,0.0003611043],"domain_scores_gemma":[0.9966469,0.001157249,0.0007357206,0.000128107,0.001065484,0.0002665055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002986858,0.0001100063,0.930858,0.000119606,0.0004133957,0.0002687948,0.0002848466,0.03044006,0.002021326,0.007888456,0.001416568,0.02588018],"study_design_scores_gemma":[0.000006745892,0.00005581567,0.9841428,0.0000500475,0.00009332936,0.0000359434,0.001123652,0.008014957,0.00105263,0.00290699,0.002492368,0.0000247327],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712147,0.003907684,0.001575319,0.003351601,0.00002400305,0.00002303521,0.0008000074,0.00001911125,0.01908464],"genre_scores_gemma":[0.9975157,0.0004100964,0.0003273246,0.00008294542,0.00001169399,0.000002504333,0.0002006767,0.000004059611,0.001445194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4740254,"threshold_uncertainty_score":0.953634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004948298023219328,"score_gpt":0.1788805399036104,"score_spread":0.1739322418803911,"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."}}