{"id":"W2770531307","doi":"10.3390/su9122080","title":"Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry","year":2017,"lang":"en","type":"article","venue":"Sustainability","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China; Innovation, Science and Economic Development Canada","keywords":"Data envelopment analysis; Interval (graph theory); Distribution (mathematics); Constraint (computer-aided design); Environmental pollution; Normal distribution; Mathematical optimization; Computer science; Mathematics; Econometrics; Statistics; Environmental science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.003587969,0.0008593234,0.001035663,0.001617606,0.0003993783,0.002169033,0.000849325,0.0007335302,0.0009826312],"category_scores_gemma":[0.005425286,0.0003936748,0.001382802,0.001731866,0.0006015392,0.001564086,0.0009302394,0.0007389111,0.00009445037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002075822,"about_ca_system_score_gemma":0.001702982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01027437,"about_ca_topic_score_gemma":0.002754645,"domain_scores_codex":[0.9980143,0.0008534461,0.0001082654,0.0002632885,0.0005129097,0.0002477491],"domain_scores_gemma":[0.9980972,0.001193826,0.0001701842,0.0001161755,0.0003798502,0.00004279681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001821116,0.00002313532,0.001339999,0.00003522121,0.00002871542,0.0000401421,0.00003492026,0.9861788,0.0003953033,0.006790545,0.00009918244,0.005015785],"study_design_scores_gemma":[0.000001383807,0.00001109806,0.0003193059,0.000004145489,0.00000580009,0.000006036278,0.00001885841,0.9978701,0.000189855,0.001484736,0.0000851149,0.000003603897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2074486,0.0004453232,0.7833005,0.0002409736,0.00002140611,0.00007876226,0.0001470562,0.0001074833,0.008209838],"genre_scores_gemma":[0.983648,0.0002350893,0.01519789,0.0000146064,0.000006540554,0.00007161057,0.0001025438,0.00001530972,0.0007083372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01027437,"threshold_uncertainty_score":0.02042913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080574342799985,"score_gpt":0.3803485429675188,"score_spread":0.2722911086875203,"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."}}