{"id":"W4390204993","doi":"10.1016/j.jenvman.2023.119883","title":"Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis","year":2023,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Beijing Nova Program; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Multiple-criteria decision analysis; Biofuel; Purchasing; Supply chain; Environmental economics; Production (economics); Subsidy; Business; Operations research; Engineering; Operations management; Economics; Waste management","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":[],"consensus_categories":[],"category_scores_codex":[0.0003817049,0.0001193039,0.0001691546,0.0005170425,0.00003990518,0.00002729993,0.0001216023,0.00002798721,0.00008171584],"category_scores_gemma":[0.000002028036,0.00008081774,0.00008481765,0.0005684948,0.00006838067,0.0001026196,0.00003904047,0.00006617248,0.000002703658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007104559,"about_ca_system_score_gemma":0.00000274228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003669352,"about_ca_topic_score_gemma":0.0000180718,"domain_scores_codex":[0.9990976,0.00004753459,0.0002914511,0.0001028437,0.0003489042,0.0001116545],"domain_scores_gemma":[0.9996062,0.00002966271,0.0001706286,0.0001436372,0.00001004605,0.00003984766],"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":[0.00004672299,0.00004917135,0.001713435,0.00004948804,0.0006415666,0.0000119952,0.0001995,0.9890122,0.0009866352,0.0005093321,0.0008660152,0.005913949],"study_design_scores_gemma":[0.001068854,0.0002202927,0.08957403,0.00006979952,0.0009374559,0.00000489816,0.001986559,0.8993322,0.001678701,0.00004293287,0.004907096,0.0001771853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.084549,0.0001775962,0.9135503,0.0009708296,0.00007256724,0.0003020921,0.000009793909,0.00003557257,0.0003322446],"genre_scores_gemma":[0.9816901,0.0006340235,0.01726637,0.0000861493,0.00001604713,0.000009054228,0.00004018683,0.00001682238,0.0002412156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8971411,"threshold_uncertainty_score":0.3295652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287986752086977,"score_gpt":0.2268323314568015,"score_spread":0.2139524639359318,"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."}}