{"id":"W4408538117","doi":"10.1016/j.aei.2025.103263","title":"LLM-MANUF: An integrated framework of Fine-Tuning large language models for intelligent Decision-Making in manufacturing","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; Chongqing Science and Technology Commission; Fundamental Research Funds for the Central Universities; Ministry of Science and Technology of the People's Republic of China","keywords":"Computer science; Decision-making models; Group decision-making; Manufacturing engineering; Artificial intelligence; Industrial engineering; Engineering","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":[0.0002151943,0.0002492463,0.0003160479,0.0004674013,0.00003993636,0.00004264606,0.0002567601,0.0001492739,0.000007680082],"category_scores_gemma":[0.0001913231,0.0002606085,0.00005906327,0.0002963876,0.000008524083,0.0007664108,0.00005475123,0.0002898544,9.57481e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001264219,"about_ca_system_score_gemma":0.00001776734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000025718,"about_ca_topic_score_gemma":0.000009804422,"domain_scores_codex":[0.9986549,0.000004341849,0.00072386,0.0001138037,0.0001386715,0.0003643927],"domain_scores_gemma":[0.9991832,0.00032497,0.00007880524,0.0003002284,0.00006569913,0.0000470842],"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":[0.00002064052,0.00002052254,0.000005627237,0.0007596933,0.00002242307,8.830767e-7,0.003173775,0.9011679,0.00002180811,0.002373552,0.000005747322,0.09242743],"study_design_scores_gemma":[0.0003320483,0.00002811557,0.00003046947,0.001539498,0.00001299835,9.758736e-7,0.001198962,0.9833415,0.01059472,0.00131361,0.001365373,0.0002417966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06905269,0.0003212318,0.9293675,0.000002956931,0.0003234535,0.0002892838,0.00002279583,0.0002976886,0.0003223819],"genre_scores_gemma":[0.5778725,0.0000704234,0.4219268,0.00002377277,0.00001090907,0.00003489853,0.00002810927,0.00002684449,0.000005801124],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5088198,"threshold_uncertainty_score":0.9999846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005217509720654744,"score_gpt":0.2477513650644858,"score_spread":0.242533855343831,"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."}}