{"id":"W4213371707","doi":"10.55365/1923.x2021.19.17","title":"Efficiency Analysis of Large Global Manufacturing Companies by Data Envelopment Analysis Approach","year":2021,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Manufacturing; Term (time); Process (computing); Operations research; Computer science; Efficiency; Envelopment; Operations management; Econometrics; Industrial organization; Economics; Business; Marketing; Engineering; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003506638,0.0001626465,0.001639135,0.000276741,0.0000924159,0.00006730516,0.001099003,0.00004566068,0.00007662042],"category_scores_gemma":[0.0004129938,0.000135629,0.0004421791,0.003488727,0.0001228257,0.0001777942,0.0005822199,0.00005921729,0.000004316668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003643706,"about_ca_system_score_gemma":0.0001013562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003871899,"about_ca_topic_score_gemma":0.0001108479,"domain_scores_codex":[0.9970034,0.0001320789,0.001482118,0.0008731008,0.0002944626,0.0002148733],"domain_scores_gemma":[0.9967483,0.0002634203,0.001038601,0.001727808,0.0001788358,0.00004300244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004745404,0.003870876,0.1640103,0.003718469,0.02851474,0.00001686456,0.0009210367,0.324213,0.00006625574,0.08703367,0.01011625,0.3774711],"study_design_scores_gemma":[0.0002746373,0.00002352893,0.05940773,0.0003225786,0.008601096,0.000003476801,0.0002351102,0.8192812,0.0004296836,0.0006234071,0.1103322,0.0004653086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7473857,0.222281,0.0258388,0.0003753313,0.00005856607,0.0001416888,0.001846824,0.000005550888,0.002066538],"genre_scores_gemma":[0.7851632,0.2102842,0.003982184,0.0001897726,0.000005170857,0.000002023834,0.0002901212,0.000003288131,0.00008006789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4950682,"threshold_uncertainty_score":0.5530792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07150039997612802,"score_gpt":0.3515835892565427,"score_spread":0.2800831892804147,"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."}}