{"id":"W3100382571","doi":"10.5267/j.dsl.2020.10.003","title":"Green supplier selection using fuzzy Delphi method for developing sustainable supply chain","year":2020,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Selection (genetic algorithm); Remanufacturing; Quality (philosophy); Delphi method; Ranking (information retrieval); Supply chain; Computer science; Process management; Supplier relationship management; Process (computing); Supply chain management; Business; Marketing; Engineering; Manufacturing engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02801337,0.0009679833,0.0009066889,0.008212349,0.002998014,0.002667019,0.001093781,0.0009493958,0.005190737],"category_scores_gemma":[0.02733153,0.00094768,0.001275389,0.004349004,0.001621102,0.002331768,0.00352707,0.00107301,0.0004464852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003921467,"about_ca_system_score_gemma":0.007770239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003739571,"about_ca_topic_score_gemma":0.006563783,"domain_scores_codex":[0.9654691,0.02849147,0.001036613,0.0006088541,0.003340615,0.001053277],"domain_scores_gemma":[0.9831299,0.01286976,0.0006243085,0.0003487197,0.002718383,0.0003089037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001177011,0.001283663,0.02537195,0.004155338,0.0005197864,0.002484096,0.12278,0.1455611,0.0201204,0.1351164,0.006714342,0.5347159],"study_design_scores_gemma":[0.0004129102,0.002188161,0.02005852,0.002671183,0.000325153,0.001323391,0.2284731,0.480857,0.01845682,0.2032521,0.0412988,0.0006829025],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3038178,0.0005511996,0.6572703,0.001832836,0.00007209495,0.008677274,0.0002484816,0.0001235709,0.02740646],"genre_scores_gemma":[0.514965,0.0004660642,0.4777154,0.0001889676,0.0000162799,0.004465981,0.0001154527,0.00001997401,0.002046953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02801337,"threshold_uncertainty_score":0.1481507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03522603766950656,"score_gpt":0.2995121357361398,"score_spread":0.2642860980666333,"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."}}