{"id":"W4407948873","doi":"10.1109/jiot.2025.3546124","title":"Intelligent and Autonomous Edge Slicing for IoT Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Slicing; Internet of Things; Enhanced Data Rates for GSM Evolution; Edge computing; Distributed computing; Computer network; Computer security; Artificial intelligence; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.0009093606,0.000130522,0.0002567536,0.0002514768,0.0001149428,0.0004162251,0.0007778371,0.00006423463,7.36955e-7],"category_scores_gemma":[0.00008170106,0.0001138801,0.0001073018,0.0001245878,0.00003622442,0.0001872905,0.0002040876,0.000274225,0.000002135177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009364512,"about_ca_system_score_gemma":0.0001085572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007923375,"about_ca_topic_score_gemma":4.372669e-7,"domain_scores_codex":[0.9988268,0.00003831682,0.0005102418,0.0002096384,0.0001490658,0.0002659143],"domain_scores_gemma":[0.9990712,0.0001999424,0.0002829789,0.0001709279,0.0001959495,0.00007903275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001567687,0.0002892427,0.003887145,0.001161061,0.0008542486,0.00009493113,0.04230781,0.002761345,0.01605109,0.04976794,0.2337136,0.6489548],"study_design_scores_gemma":[0.0005395827,0.0002643862,0.0001422622,0.001353701,0.00003096789,0.0004923151,0.0001209004,0.9311962,0.02733,0.005996116,0.03228814,0.0002454283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1398455,0.0007196842,0.8355,0.0005657357,0.02207768,0.0001196656,7.497007e-8,0.00004417947,0.001127465],"genre_scores_gemma":[0.9713505,0.00001303674,0.02449099,0.0004221568,0.001188398,0.000003663831,1.398266e-7,0.00001025626,0.002520849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9284348,"threshold_uncertainty_score":0.4643896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01927380349719957,"score_gpt":0.2697694070002669,"score_spread":0.2504956035030673,"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."}}