{"id":"W1943157783","doi":"10.5539/jas.v7n9p160","title":"Resource Use Efficiency of Tea Production in Vietnam: Using Translog SFA Model","year":2015,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ecolab","keywords":"Tobit model; Production (economics); Production–possibility frontier; Resource use; Resource (disambiguation); Business; Productivity; Agriculture; Resource productivity; Agricultural economics; Scarcity; Stochastic frontier analysis; Natural resource economics; Economics; Environmental economics; Agricultural science; Resource allocation; Environmental science; Economic growth; Geography; Econometrics; Microeconomics; Computer science","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":[0.001464925,0.0006457801,0.0007027622,0.0009279909,0.0005785241,0.001925462,0.0009168906,0.0007690873,0.00351118],"category_scores_gemma":[0.00237021,0.0003829842,0.001485533,0.001102671,0.0004455156,0.0007751184,0.0007240456,0.0009402685,0.0003271895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00286004,"about_ca_system_score_gemma":0.002469622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1543605,"about_ca_topic_score_gemma":0.06965628,"domain_scores_codex":[0.9993597,0.0002919196,0.00002922231,0.0001136536,0.00004717012,0.0001584006],"domain_scores_gemma":[0.9983363,0.001160379,0.0001594809,0.00005350439,0.0001903917,0.0001000446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002334957,0.0004004861,0.1092296,0.0002644072,0.0004243666,0.001640628,0.001175054,0.8584145,0.0007813131,0.009618155,0.002485358,0.01533255],"study_design_scores_gemma":[0.00001920302,0.0001033512,0.01803199,0.00003746616,0.00007712258,0.00005335135,0.0009405338,0.9779735,0.0001062058,0.001430181,0.001205481,0.00002150862],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723632,0.0004053851,0.01848665,0.0007686778,0.00004234361,0.0001074423,0.001159774,0.00007412319,0.006592389],"genre_scores_gemma":[0.9947188,0.0002690121,0.00214673,0.00002863107,0.00001008038,0.00005727773,0.0005336344,0.00001367195,0.002222131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1543605,"threshold_uncertainty_score":0.3069241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1471742496574946,"score_gpt":0.366836342979757,"score_spread":0.2196620933222625,"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."}}