{"id":"W4388077030","doi":"10.1109/tce.2023.3328949","title":"Sustainable Edge Node Computing Deployments in Distributed Manufacturing Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Heriot-Watt University; University of Surrey; Royal Academy of Engineering; Queen's University; University of Essex; Newcastle University; University of West London; Chalmers Tekniska Högskola; Cranfield University; Queen's University Belfast; University College London","keywords":"Computer science; Edge computing; Node (physics); Enhanced Data Rates for GSM Evolution; Distributed computing; Computer network; Engineering; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006564739,0.0003149264,0.0002853277,0.0003672708,0.0006397881,0.0006325829,0.000632336,0.0006082875,0.001072843],"category_scores_gemma":[0.001270126,0.0001944793,0.0002568812,0.0004069711,0.0004960369,0.001008758,0.0008947853,0.0003731761,0.0001460715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007930052,"about_ca_system_score_gemma":0.0004305134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001535207,"about_ca_topic_score_gemma":0.002332435,"domain_scores_codex":[0.9996394,0.0001441295,0.00001249651,0.0000578539,0.00008009562,0.00006597855],"domain_scores_gemma":[0.9995731,0.0002243386,0.00004612411,0.00005439354,0.000061192,0.00004087696],"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":[0.00009018849,0.0000471258,0.001322503,0.00005365454,0.00001803285,0.0002579279,0.00009697531,0.9245123,0.008052907,0.03399697,0.0006563937,0.03089503],"study_design_scores_gemma":[0.000007176535,0.00007484137,0.0003443469,0.000007024908,0.000007193433,0.00005203924,0.00008674955,0.9867264,0.002407206,0.008910844,0.001369597,0.000006628507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2368059,0.0007083739,0.7510284,0.0005040283,0.00007619391,0.00008753855,0.00004952381,0.0002615413,0.01047843],"genre_scores_gemma":[0.9699023,0.0001688166,0.02882309,0.00003650741,0.000005304374,0.00002271826,0.0000161508,0.000009645441,0.001015472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001535207,"threshold_uncertainty_score":0.005753696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01318487970442547,"score_gpt":0.2291673082237236,"score_spread":0.2159824285192981,"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."}}