{"id":"W4319073581","doi":"10.1007/978-3-031-17629-6","title":"Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus","year":2023,"lang":"en","type":"book","venue":"Lecture notes in mechanical engineering","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Technical University of Athens; Wuhan University of Technology; Wuhan University; Tallinna Tehnikaülikool; Shanghai Jiao Tong University; Università degli Studi di Genova; Università degli Studi di Padova; Universidade de Coimbra; Concordia University; Università di Catania; Technische Universität Kaiserslautern; Consiglio Nazionale delle Ricerche; Universitetet i Stavanger; Università degli Studi di Cagliari; Iowa State University; Texas Tech University; Huazhong University of Science and Technology; University of Patras; University of Hong Kong; University of Bristol; Oregon State University; University of Texas at San Antonio; Koç Üniversitesi; Universidade de Aveiro; National and Kapodistrian University of Athens; Wayne State University; Old Dominion University","keywords":"Nexus (standard); Automation; Manufacturing engineering; Computer science; Engineering; Embedded system; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002709689,0.0004631867,0.0004202249,0.0005071691,0.00005792859,0.0001253787,0.000759481,0.001026914,0.0000378497],"category_scores_gemma":[0.0001616531,0.0004095783,0.00005946285,0.00028397,0.00004193802,0.0001976927,0.0002668912,0.001880209,0.00007378896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002801049,"about_ca_system_score_gemma":0.00003143224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002329319,"about_ca_topic_score_gemma":0.00001383369,"domain_scores_codex":[0.9983032,0.000009605227,0.0005735987,0.000393335,0.0002810326,0.0004391794],"domain_scores_gemma":[0.9987074,0.0003729271,0.0000517865,0.0007768326,0.00001867144,0.00007240823],"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":[0.000002076203,0.000008501872,7.275413e-7,0.0008119701,0.000134713,0.00002983007,0.0001290727,0.9169245,0.0003775794,0.01709315,0.0007736743,0.06371415],"study_design_scores_gemma":[0.0004812269,0.00007581168,0.0000263535,0.001689789,0.0001170693,0.0001049671,0.00002973151,0.7345885,0.04973738,0.1387811,0.07280167,0.001566376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007158377,0.001227297,0.9722247,0.0005444671,0.002218235,0.0009962216,0.000129974,0.005545661,0.01639767],"genre_scores_gemma":[0.9694524,0.001090095,0.007060682,0.0002763804,0.002142705,0.000523689,0.002051378,0.001535618,0.01586702],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9687366,"threshold_uncertainty_score":0.9998356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02810385987467574,"score_gpt":0.2514903981847319,"score_spread":0.2233865383100562,"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."}}