{"id":"W7152617570","doi":"10.1109/icecet63943.2025.11472102","title":"Optimizing Telehealth with Fog-Cloud IoT: Innovations in Data Integration and Energy Efficiency","year":2025,"lang":"","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Efficient energy use; Data integration; Telehealth; System integration; Energy (signal processing); Energy consumption","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004147838,0.0005249532,0.0004071297,0.0003102749,0.000358343,0.00128926,0.001175154,0.0005602596,0.001148398],"category_scores_gemma":[0.0008949989,0.0002186786,0.0003165561,0.0006821878,0.0003709918,0.001962008,0.0008772931,0.0006083794,0.0002501553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005353104,"about_ca_system_score_gemma":0.0005831262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001862077,"about_ca_topic_score_gemma":0.00275421,"domain_scores_codex":[0.9997799,0.00003563403,0.00001341852,0.00004810299,0.0000792165,0.00004364495],"domain_scores_gemma":[0.9997508,0.00009262889,0.00002154197,0.0000545837,0.00006193914,0.00001847029],"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":[0.0006044761,0.0006205578,0.0102699,0.0005063644,0.0002395116,0.0007248644,0.0003029998,0.2846573,0.07808316,0.07263138,0.01456149,0.5367979],"study_design_scores_gemma":[0.00002828973,0.0001470722,0.001673844,0.00004156652,0.00005265787,0.0002589297,0.00009681718,0.9501017,0.01456778,0.02270148,0.01030686,0.00002283338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06893783,0.002452448,0.9150949,0.001684279,0.0002507885,0.0001147483,0.000181371,0.0006916697,0.01059198],"genre_scores_gemma":[0.8697971,0.001795699,0.1256271,0.0004945609,0.000205077,0.00005288803,0.0001893749,0.0001023447,0.00173593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001862077,"threshold_uncertainty_score":0.003883958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864021725086676,"score_gpt":0.282263671212752,"score_spread":0.2536234539618852,"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."}}