{"id":"W4285099486","doi":"10.18280/jesa.550315","title":"Selection Algorithm for Reducing IoT Service Delay in the Smart Factory","year":2022,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Innovation in Digital Healthcare Systems","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Server; Computer science; Factory (object-oriented programming); Cloud computing; Enhanced Data Rates for GSM Evolution; Edge computing; Scheduling (production processes); Digitization; Real-time computing; Computer network; Quality of service; Edge device; Algorithm; Engineering; Operating system; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005643989,0.00023348,0.0003814168,0.0004688612,0.004129424,0.00009709647,0.0005599231,0.0001234265,0.0002948516],"category_scores_gemma":[0.0002551238,0.0001851729,0.000110635,0.00161288,0.00003249372,0.0003007155,0.0001452097,0.001838204,0.00007978223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001936734,"about_ca_system_score_gemma":0.0009498553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008915178,"about_ca_topic_score_gemma":0.0003593632,"domain_scores_codex":[0.9931875,0.003282611,0.001651497,0.0002948271,0.0007960095,0.0007875414],"domain_scores_gemma":[0.9967141,0.001035232,0.001062449,0.0002897446,0.0007894766,0.0001090097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001524172,0.0003679168,0.02957642,0.002899022,0.000173154,0.0001317177,0.06653976,0.003187033,0.0003948037,0.005380596,0.1026272,0.78857],"study_design_scores_gemma":[0.003565976,0.00122837,0.5430758,0.001752356,0.00005926094,0.004584404,0.05521929,0.1866933,0.00002978384,0.006720398,0.1961639,0.0009071399],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9258057,0.0008202671,0.03450352,0.005997138,0.009892451,0.006343647,0.00030919,0.0006326333,0.01569548],"genre_scores_gemma":[0.986257,0.000008994083,0.005331522,0.003805559,0.001202608,0.0009791842,0.00004098107,0.0001043773,0.002269837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7876629,"threshold_uncertainty_score":0.9971671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07371986060885662,"score_gpt":0.3813364622872046,"score_spread":0.307616601678348,"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."}}