{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01712353,0.001534434,0.00467683,0.01850483,0.001027117,0.003895683,0.002090161,0.002083489,0.005049751],"category_scores_gemma":[0.05378984,0.00114937,0.00472461,0.01786588,0.001434291,0.005634931,0.002731392,0.001867083,0.0006932387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004337072,"about_ca_system_score_gemma":0.025304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007826301,"about_ca_topic_score_gemma":0.02749445,"domain_scores_codex":[0.9898877,0.004322603,0.003078936,0.0006287941,0.001780986,0.0003010004],"domain_scores_gemma":[0.9481802,0.03940804,0.005336088,0.001107523,0.005521297,0.0004468019],"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":[0.00008392746,0.00005047578,0.000586177,0.8021165,0.001768929,0.0002327966,0.0008503895,0.0002351923,0.0003011839,0.002672053,0.004068609,0.1870337],"study_design_scores_gemma":[0.00005127305,0.0001253293,0.001372376,0.8912034,0.009635387,0.0004847877,0.001128439,0.000138534,0.0002403535,0.001906483,0.09366639,0.00004723242],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004141376,0.9976797,0.0004828463,0.0005372518,0.0001236581,0.0002225592,0.0001103478,0.000008687904,0.0004209986],"genre_scores_gemma":[0.005381729,0.9917845,0.001580634,0.0004984944,0.0000654601,0.0004295307,0.0001232664,0.000006207585,0.0001302337],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01850483,"threshold_uncertainty_score":0.09055901,"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."}}