{"id":"W4413929974","doi":"10.1016/j.mfglet.2025.06.187","title":"A Fuzzy Data-Driven framework for Enhanced risk management Decision-Making in Manufacturing: A Case study","year":2025,"lang":"en","type":"article","venue":"Manufacturing Letters","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Fuzzy logic; Computer science; Risk management; Artificial intelligence; Business","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":[0.005525117,0.00067897,0.001015326,0.002522577,0.0007189516,0.001483976,0.004272625,0.0002053159,0.0001694207],"category_scores_gemma":[0.00354925,0.0005838696,0.0002837569,0.0008301599,0.0001056524,0.0008060681,0.003314995,0.000807935,0.0001445647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003449873,"about_ca_system_score_gemma":0.00004007207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001752722,"about_ca_topic_score_gemma":0.0006660595,"domain_scores_codex":[0.9913518,0.0005214462,0.00221159,0.002884192,0.001945618,0.001085324],"domain_scores_gemma":[0.9819385,0.0122091,0.0007137924,0.004890442,0.00008553937,0.0001626708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008356074,0.0006361246,0.007687837,0.0000907788,0.0003598121,0.008574282,0.003802323,0.05306996,0.0001202433,0.0002695408,0.00666996,0.9178835],"study_design_scores_gemma":[0.02173536,0.0005372703,0.2572179,0.005280243,0.001102589,0.0007240233,0.06385062,0.03480026,0.01692075,0.5494663,0.04241627,0.00594837],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6034437,0.00003111623,0.3925291,0.0003941476,0.001310248,0.001789656,0.00005793951,0.0001085396,0.0003354667],"genre_scores_gemma":[0.8402262,0.000007617763,0.1575978,0.001619517,0.0001422475,0.0002387221,0.000006654995,0.00005562262,0.0001055925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9119352,"threshold_uncertainty_score":0.9996613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09339305322206183,"score_gpt":0.4277141620038086,"score_spread":0.3343211087817468,"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."}}