{"id":"W4414453564","doi":"10.1007/s40815-025-02065-2","title":"Multi-attribute Decision-Making Methods to Low-Carbon Green Supply Chain Management in N-Cubic Fuzzy Aggregation Operators","year":2025,"lang":"en","type":"article","venue":"International Journal of Fuzzy Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Supply chain; Fuzzy logic; Process (computing); Supply chain management; Ecological footprint; Ambiguity; Computational intelligence; Work (physics)","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01501333,0.0003688809,0.0008933167,0.005566385,0.0001135768,0.001257325,0.003463138,0.0001840662,0.00007936093],"category_scores_gemma":[0.007418742,0.0002967182,0.0003338576,0.002326995,0.00005225737,0.0007451396,0.0008010481,0.000438544,0.00008823734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009420178,"about_ca_system_score_gemma":0.0001994718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001606678,"about_ca_topic_score_gemma":0.0001343109,"domain_scores_codex":[0.9896368,0.001316422,0.00383321,0.0007494567,0.004011542,0.0004525273],"domain_scores_gemma":[0.9910481,0.00390213,0.001391451,0.0007255954,0.00270853,0.0002242023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000775743,0.0003926528,0.05070114,0.00004142934,0.0005318597,0.001291901,0.001660796,0.05127942,0.005155247,0.003292831,0.003680104,0.8811969],"study_design_scores_gemma":[0.0207639,0.0005742636,0.4398724,0.03124316,0.0002597128,0.001305361,0.01770842,0.3400469,0.003470951,0.05361533,0.08828425,0.002855371],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5671865,0.001199007,0.4078852,0.002069176,0.01829705,0.001002079,0.00004086053,0.00003540043,0.002284806],"genre_scores_gemma":[0.8932944,0.00003735724,0.1046762,0.0004899935,0.0004247406,0.00003303638,0.00000215932,0.00002710562,0.001014967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8783415,"threshold_uncertainty_score":0.9999485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06767104061369232,"score_gpt":0.4520419226116101,"score_spread":0.3843708819979177,"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."}}