{"id":"W2029369144","doi":"10.1002/nav.20308","title":"DEA models for two‐stage processes: Game approach and efficiency decomposition","year":2008,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":526,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Data envelopment analysis; Decomposition; Set (abstract data type); Process (computing); Measure (data warehouse); Computer science; Mathematical optimization; Stage (stratigraphy); Efficiency; Decomposition method (queueing theory); Mathematics; Statistics; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01274437,0.0002737546,0.0005387799,0.001086769,0.001299497,0.0005899072,0.001533807,0.0001894229,0.00003352047],"category_scores_gemma":[0.03078735,0.0002121956,0.0001480016,0.003431226,0.00171336,0.0004519637,0.0004429469,0.0006054866,0.00009666368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001781563,"about_ca_system_score_gemma":0.0009067825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001747352,"about_ca_topic_score_gemma":0.00006371247,"domain_scores_codex":[0.9913809,0.0006411375,0.0009189987,0.001344514,0.004605154,0.001109331],"domain_scores_gemma":[0.9864591,0.008042063,0.000237031,0.0009807626,0.003901232,0.0003797832],"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":[0.001015377,0.003021899,0.003368797,0.0005377186,0.000116985,0.0002252689,0.006436738,0.7997229,0.005650291,0.1534213,0.0103258,0.01615699],"study_design_scores_gemma":[0.0007961986,0.0003943799,0.0002238271,0.00002572261,0.00002171038,0.00004467876,0.0005023944,0.9185651,0.000844798,0.07631882,0.001967146,0.0002952332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1131896,0.0007560428,0.8715434,0.0002568872,0.00009049116,0.0008148916,0.00009168217,0.00007335572,0.01318361],"genre_scores_gemma":[0.9779269,0.0001247662,0.01891794,0.00007409156,0.0001481023,0.00009723988,0.00003095567,0.00003112343,0.002648939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8647372,"threshold_uncertainty_score":0.9994812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4504223733606303,"score_gpt":0.5233901437995412,"score_spread":0.0729677704389109,"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."}}