{"id":"W4312198933","doi":"10.1109/ieem55944.2022.9989843","title":"Sociotechnical System Digital Twin as an Organizational-enhancer Applied to Helicopter Engines Maintenance","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"Safran; Association Nationale de la Recherche et de la Technologie","keywords":"Flexibility (engineering); Computer science; Sociotechnical system; Modularity (biology); Field (mathematics); Reinforcement learning; Context (archaeology); Human–computer interaction; Knowledge management; Systems engineering; Engineering; Artificial intelligence","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":[0.0002345041,0.0004481312,0.0003099835,0.0005895004,0.0001268665,0.0004178901,0.0006367442,0.0001626114,0.0003502346],"category_scores_gemma":[0.00003141497,0.0005386397,0.00007089812,0.0005475123,0.00002004754,0.0004410741,0.0001718248,0.0007920415,0.0001034775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000599215,"about_ca_system_score_gemma":0.00002578867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004159393,"about_ca_topic_score_gemma":2.485946e-7,"domain_scores_codex":[0.9977198,0.00001018913,0.000536857,0.0004859955,0.0007721258,0.0004750051],"domain_scores_gemma":[0.9992553,0.00005099862,0.00005009997,0.0003176118,0.00006780512,0.0002581994],"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":[0.00004220496,0.00004471698,0.00001166516,0.00009688575,0.000183105,0.00002505267,0.0001419943,0.8866554,0.001975475,0.1064525,0.001905986,0.002464966],"study_design_scores_gemma":[0.002920844,0.0004011397,0.0002164142,0.0005826056,0.00007688796,0.0001219945,0.002373505,0.8916172,0.00374878,0.0002239834,0.09541722,0.002299477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6043794,0.00007526003,0.1695247,0.001923559,0.02305706,0.003455992,0.001419822,0.009170521,0.1869936],"genre_scores_gemma":[0.9974081,0.00002224816,0.0004166563,0.00007779906,0.0005201473,0.0004945707,0.0001693037,0.0001182781,0.0007728995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3930287,"threshold_uncertainty_score":0.9997065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02422050375099142,"score_gpt":0.2241041660807317,"score_spread":0.1998836623297403,"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."}}