{"id":"W4413038281","doi":"10.3390/computers14080318","title":"Integrating Large Language Models into Digital Manufacturing: A Systematic Review and Research Agenda","year":2025,"lang":"en","type":"review","venue":"Computers","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Canada Research Chairs","keywords":"Structuring; Context (archaeology); Inclusion (mineral); Process (computing); Thematic analysis; Phenomenon; Knowledge management; Data science; Engineering ethics; Management science; Political science; Computer science; Sociology; Engineering; Social science; Qualitative research; Geography; Epistemology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000518608,0.000411473,0.001706518,0.0003935324,0.00008100204,0.0005027553,0.0005344534,0.0002203476,0.000006346369],"category_scores_gemma":[0.00008980023,0.0003395529,0.0002454714,0.0003822155,0.00003935543,0.000554415,0.0002204178,0.0008846781,0.00005948437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002131712,"about_ca_system_score_gemma":0.00006967139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000158198,"about_ca_topic_score_gemma":6.862221e-7,"domain_scores_codex":[0.9980459,0.0001099053,0.0008777303,0.0002890438,0.0002932129,0.0003841569],"domain_scores_gemma":[0.9987449,0.0005390178,0.00008293721,0.0004832396,0.00004457938,0.0001053434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[6.197725e-8,0.000004895196,5.522957e-9,0.8671924,0.0001204753,0.00001149455,0.0002915737,0.00003356711,2.489216e-9,0.0003178954,0.002598786,0.1294289],"study_design_scores_gemma":[0.00005661078,0.000007927071,4.679469e-9,0.8743107,0.0003465181,0.00004027291,0.0001716236,0.0015273,5.471517e-7,0.0001044324,0.1231061,0.0003280319],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[3.857878e-7,0.966929,0.01393702,0.00001029729,0.0002205251,0.001741317,0.0001064697,0.0003130879,0.01674191],"genre_scores_gemma":[0.00005531308,0.9986054,0.0003556946,0.00004482725,0.00004036009,0.0002506334,0.0001682453,0.00005698198,0.0004224881],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1291008,"threshold_uncertainty_score":0.9999056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05911579953668723,"score_gpt":0.3451806282077306,"score_spread":0.2860648286710434,"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."}}