{"id":"W4387217682","doi":"10.1007/978-3-031-43181-4_3","title":"Incorporating Preference Information in DEA-R and DEA for Efficiency Analysis","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in big data","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Preference; Data envelopment analysis; Decision maker; Measure (data warehouse); Value (mathematics); Econometrics; Process (computing); Mathematics; Computer science; Economics; Statistics; Operations research; 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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008435844,0.0003721164,0.001198444,0.003613791,0.0002519459,0.0002669439,0.002162024,0.0002274212,0.00000846607],"category_scores_gemma":[0.0124341,0.0002967802,0.0001302383,0.00254878,0.00043536,0.0007655638,0.003027848,0.0003480145,0.00008310377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001260908,"about_ca_system_score_gemma":0.0001547241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001637702,"about_ca_topic_score_gemma":0.01231671,"domain_scores_codex":[0.9949855,0.00009555635,0.00198213,0.001158435,0.001421185,0.0003571943],"domain_scores_gemma":[0.9926211,0.003486765,0.001116978,0.002164859,0.0005554691,0.00005477714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000295161,0.0002391616,0.05326977,0.0009845045,0.004675392,0.00012182,0.02153934,0.0302668,0.00000700556,0.1215547,0.0673135,0.6997329],"study_design_scores_gemma":[0.002367067,0.000372823,0.01363147,0.001725198,0.003224365,0.000007500631,0.0142827,0.3034491,0.000007321657,0.550021,0.1077776,0.003133821],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06662531,0.06159416,0.2654489,0.01030215,0.02011192,0.01847574,0.03992683,0.001592819,0.5159222],"genre_scores_gemma":[0.9191546,0.009554999,0.009568666,0.0007202646,0.0005949645,0.0002275314,0.005047087,0.0001268168,0.05500511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8525292,"threshold_uncertainty_score":0.9999484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5766136804843713,"score_gpt":0.4529227649714248,"score_spread":0.1236909155129465,"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."}}