{"id":"W4405213157","doi":"10.1007/s12351-024-00873-2","title":"Pareto-optimal peer evaluation in context-dependent DEA","year":2024,"lang":"en","type":"article","venue":"Operational Research","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; McGill University","funders":"","keywords":"Attractiveness; Context (archaeology); Data envelopment analysis; Computer science; Computational intelligence; Pareto principle; Dominance (genetics); Machine learning; Mathematical optimization; Mathematics; Psychology","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","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.06212415,0.0001024077,0.0001830046,0.001279969,0.0003141672,0.001865701,0.0007007413,0.00009173842,0.005790147],"category_scores_gemma":[0.02251764,0.00007603315,0.0001049591,0.002773,0.0001775705,0.0006182736,0.0001831426,0.0005496723,0.004999783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004317035,"about_ca_system_score_gemma":0.001494988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003163003,"about_ca_topic_score_gemma":0.001352528,"domain_scores_codex":[0.9824434,0.00162063,0.0006363284,0.0007961949,0.01410188,0.000401582],"domain_scores_gemma":[0.9915314,0.003989667,0.00002297497,0.0004998408,0.003850593,0.0001054865],"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":[0.0001159581,0.0003903218,0.01044265,0.00001406978,0.0000987644,0.0001843424,0.005157431,0.3434213,0.004922325,0.1555195,0.1106554,0.369078],"study_design_scores_gemma":[0.0002312125,0.00006143229,0.005919444,0.00003288135,0.000008074588,0.000007698472,0.001305615,0.9088441,0.0005645089,0.01251326,0.07038995,0.0001218262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9271634,0.003479194,0.006131772,0.02784467,0.0005563364,0.0007512079,0.00003864917,0.00004286936,0.03399194],"genre_scores_gemma":[0.9807552,0.00001057574,0.0002926748,0.0001176802,0.0001589306,0.00009241211,0.00003010359,0.00001049819,0.01853191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5654228,"threshold_uncertainty_score":0.9991705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3683363571449461,"score_gpt":0.5589452745761226,"score_spread":0.1906089174311765,"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."}}