{"id":"W2132912951","doi":"10.1111/j.1744-7976.2012.01264.x","title":"Technical Efficiency and Producers’ Individual Technology: Accounting for Within and Between Regional Farm Heterogeneity","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Agricultural Economics/Revue canadienne d agroeconomie","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Leibniz-Gemeinschaft; China Agricultural Research System; National Science Foundation","keywords":"Endowment; Production (economics); Technical change; Agriculture; Emerging technologies; Factors of production; Agricultural science; Geography; Economics; Welfare economics; Agricultural economics; Regional science; Econometrics; Computer science; Environmental science; Microeconomics; Political science; Economic growth; Productivity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.005481185,0.0005187386,0.001044902,0.001229926,0.0005180726,0.001892976,0.001028865,0.0005241018,0.001911085],"category_scores_gemma":[0.01280753,0.0002664555,0.001673018,0.002976471,0.001089839,0.001619652,0.001277738,0.0004946063,0.0002555923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001647399,"about_ca_system_score_gemma":0.001695547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03537897,"about_ca_topic_score_gemma":0.02326491,"domain_scores_codex":[0.9962625,0.001183074,0.0002478003,0.001186347,0.0004706977,0.000649683],"domain_scores_gemma":[0.9834344,0.009378986,0.003367129,0.002739736,0.0007772564,0.0003024901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000173609,0.00008856776,0.8315485,0.000128667,0.001585468,0.000588585,0.0006518615,0.1299285,0.001803052,0.007624239,0.0004341717,0.02544469],"study_design_scores_gemma":[0.00003107504,0.0001693267,0.8896374,0.00004156671,0.0005328973,0.0001581384,0.0007654254,0.09642317,0.001327664,0.007877019,0.002991632,0.00004473031],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9680635,0.0005619563,0.02757679,0.000154464,0.00001337371,0.00005078279,0.0005017136,0.0000488309,0.003028661],"genre_scores_gemma":[0.9976009,0.00007036685,0.001501947,0.00001357559,0.000007691607,0.00001838097,0.0001934934,0.000009388188,0.0005843207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03537897,"threshold_uncertainty_score":0.07034606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08606608436434603,"score_gpt":0.2666381693322459,"score_spread":0.1805720849678999,"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."}}