{"id":"W4387217667","doi":"10.1007/978-3-031-43181-4_9","title":"Calculating Environmental, Social and Economic Efficiencies of a Two-Stage Supply Chain in DEA-R Using Genetic Algorithm","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":"Supply chain; Sustainability; Multiplier (economics); Environmental economics; Genetic algorithm; Limit (mathematics); Mathematical optimization; Economics; Computer science; Econometrics; Operations research; Mathematics; Business; Ecology; Macroeconomics; Marketing; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003832217,0.0003941774,0.001174309,0.001128082,0.0002523367,0.00008200734,0.001452357,0.0001716727,0.00003983605],"category_scores_gemma":[0.0005814966,0.000360614,0.0001090387,0.0002878634,0.001346579,0.0001440529,0.004130988,0.0003201991,0.0000405068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003340745,"about_ca_system_score_gemma":0.0001213111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007834752,"about_ca_topic_score_gemma":0.00319389,"domain_scores_codex":[0.9954895,0.0001501979,0.001582358,0.001384175,0.0009766892,0.0004170268],"domain_scores_gemma":[0.9966061,0.001285896,0.0007811681,0.001252032,0.00003196741,0.00004290772],"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":[0.0001245447,0.0005068315,0.07381678,0.0006319709,0.002196929,0.001805115,0.05507987,0.1238291,0.0006636414,0.01282393,0.006938661,0.7215826],"study_design_scores_gemma":[0.003957398,0.0002568119,0.02508202,0.001665553,0.0007523689,0.00005570726,0.04273105,0.8566927,0.00007797442,0.04856192,0.01646329,0.003703175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9329271,0.03331803,0.003988004,0.0004961604,0.003786736,0.001952175,0.01536617,0.000107182,0.008058413],"genre_scores_gemma":[0.9346279,0.009486215,0.02119452,0.0002312386,0.001385168,0.0000279233,0.0005133058,0.0002994078,0.03223434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7328636,"threshold_uncertainty_score":0.9998846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4006967982009828,"score_gpt":0.4342031500675,"score_spread":0.03350635186651724,"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."}}