{"id":"W7117323654","doi":"10.1109/tnsm.2025.3648360","title":"Data Driven Deep Neural Network Based Task Offloading on Edge Cloud Continuum","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Cloud computing; Computation offloading; Artificial neural network; Edge computing; Edge device; Enhanced Data Rates for GSM Evolution; Task (project management); Computation; Data-driven","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","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001116794,0.0008324296,0.0007395371,0.0003656299,0.002306015,0.001044093,0.002767872,0.0002567896,0.00002587131],"category_scores_gemma":[0.000002238281,0.0009063188,0.0002121822,0.002897744,0.00009996582,0.000494847,0.0002173833,0.0009763712,0.00009733035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001581169,"about_ca_system_score_gemma":0.00008645449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006592133,"about_ca_topic_score_gemma":0.0004342353,"domain_scores_codex":[0.9942914,0.0004597727,0.0009842222,0.002040799,0.0005760058,0.001647756],"domain_scores_gemma":[0.9960132,0.000524748,0.0002796609,0.002725446,0.0001417251,0.000315243],"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":[0.0001995251,0.0002948206,0.00003956839,0.0003741384,0.0004323112,0.00005915964,0.0002552106,0.6571127,0.000002287945,0.0004519881,0.03688854,0.3038897],"study_design_scores_gemma":[0.001585143,0.0002282072,0.0005223344,0.001120321,0.0005007287,0.000003165799,0.00005968615,0.8957855,0.00001295672,0.0002904977,0.09920018,0.0006912723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002295188,0.0006275109,0.9211506,0.006285161,0.06029553,0.001170171,0.000009450385,0.0003156213,0.007850774],"genre_scores_gemma":[0.9269096,0.001111294,0.01740467,0.04042355,0.01146486,0.0001188342,0.00008913948,0.0001323315,0.0023457],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9246144,"threshold_uncertainty_score":0.9999929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02310504797575832,"score_gpt":0.2468530907254463,"score_spread":0.2237480427496879,"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."}}