{"id":"W2293649545","doi":"10.1093/ajae/aaw041","title":"Agglomeration Effects in Ontario's Dairy Farming","year":2016,"lang":"en","type":"article","venue":"American Journal of Agricultural Economics","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Inefficiency; Economies of agglomeration; Agriculture; Production (economics); Dairy farming; Agricultural economics; Heteroscedasticity; Economics; Agricultural science; Business; Function (biology); Economic geography; Microeconomics; Econometrics; Geography; Environmental science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006614049,0.000117264,0.0003514324,0.0006709395,0.0008258963,0.0007769239,0.0002571035,0.0002003509,0.002564633],"category_scores_gemma":[0.002614687,0.0001437404,0.0002748999,0.001431046,0.0008500589,0.0003198269,0.0007039004,0.0001617812,0.0001000734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008957761,"about_ca_system_score_gemma":0.003581887,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7427828,"about_ca_topic_score_gemma":0.8601179,"domain_scores_codex":[0.9993468,0.000135418,0.00002555519,0.000091849,0.0002390147,0.0001613498],"domain_scores_gemma":[0.998202,0.0007734163,0.0004361517,0.00009826379,0.0003750673,0.000115111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002671352,0.00009473298,0.8859829,0.0001393243,0.0002440105,0.0006367306,0.001537701,0.06825203,0.002756436,0.01626021,0.001419667,0.02240904],"study_design_scores_gemma":[0.00002420831,0.00003606302,0.9734807,0.00001995981,0.00005788599,0.00005690621,0.001547305,0.0167838,0.000447871,0.002865252,0.004664939,0.00001517359],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907706,0.0001595628,0.001216701,0.0001963955,0.000002082293,0.00001837051,0.0003659296,0.00001008672,0.007260157],"genre_scores_gemma":[0.998957,0.00006400474,0.0001722131,0.000007634549,0.000001048031,0.000004174297,0.0000681293,0.000001091596,0.0007245935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2572172,"threshold_uncertainty_score":0.517464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02191249457255144,"score_gpt":0.2765660719298119,"score_spread":0.2546535773572605,"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."}}