{"id":"W7116313742","doi":"10.1109/tce.2025.3607745","title":"Guest Editorial Optimizing Consumer Electronics With Lightweight AI for Enhanced Decision Making at the Edge","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Internet of Things and AI","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Electronics; Enhanced Data Rates for GSM Evolution; Edge device; Electronic equipment","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001095621,0.001144485,0.0009720646,0.0005199718,0.003057699,0.001268291,0.002310784,0.0007396593,0.0001015791],"category_scores_gemma":[0.00005309782,0.0009023309,0.0007298413,0.00138286,0.0005149316,0.0008560176,0.00004674988,0.002746828,0.0001578212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001935328,"about_ca_system_score_gemma":0.002984291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003151425,"about_ca_topic_score_gemma":0.001367987,"domain_scores_codex":[0.9929031,0.0002649985,0.001271122,0.001936109,0.001163262,0.002461379],"domain_scores_gemma":[0.9940954,0.002140211,0.000556314,0.001864576,0.001125318,0.0002181153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01769637,0.003114448,0.00001249211,0.0005932102,0.006936016,0.00003819942,0.003451,0.04545066,0.02918053,0.02268466,0.295387,0.5754554],"study_design_scores_gemma":[0.003957562,0.001746073,0.000001104331,0.001130424,0.001012569,0.00005417508,0.00003544278,0.05014418,0.3350774,0.001297321,0.6043561,0.001187708],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002326959,0.009277508,0.8991197,0.002820574,0.08373837,0.001896943,0.00005277773,0.00029973,0.0004674201],"genre_scores_gemma":[0.9597247,0.006566355,0.01297314,0.003636398,0.008019319,0.000845912,0.00001000566,0.0002892574,0.007934906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9573978,"threshold_uncertainty_score":0.9997685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007237384732782284,"score_gpt":0.2645738140113055,"score_spread":0.2573364292785232,"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."}}