{"id":"W4408397200","doi":"10.1007/978-3-031-85593-1_1","title":"An Efficient Edge-Based Privacy-Preserving Range Aggregation Scheme for Aging in Place System","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Innovation in Digital Healthcare Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; Research and Productivity Council; University of New Brunswick","funders":"","keywords":"Computer science; Scheme (mathematics); Range (aeronautics); Enhanced Data Rates for GSM Evolution; Theoretical computer science; Artificial intelligence; Mathematics","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":[0.0009458326,0.0005403053,0.001379334,0.0006807739,0.001291697,0.001393094,0.001988885,0.000756141,0.002246924],"category_scores_gemma":[0.002036986,0.0002345715,0.0006308733,0.001912222,0.0005063998,0.00329875,0.00311915,0.0009844707,0.0006641691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008143236,"about_ca_system_score_gemma":0.001222589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001700535,"about_ca_topic_score_gemma":0.001486942,"domain_scores_codex":[0.9985166,0.0003048815,0.0001153427,0.0002779162,0.0005161159,0.0002690626],"domain_scores_gemma":[0.998456,0.0003063628,0.0001061379,0.0007421267,0.0003059523,0.00008342521],"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":[0.002541741,0.0005771843,0.003297384,0.0005143526,0.0002414613,0.0006129125,0.001129592,0.1217432,0.07740458,0.1060857,0.02693467,0.6589173],"study_design_scores_gemma":[0.0001204593,0.0005733032,0.001594722,0.0000330446,0.0001458367,0.000930146,0.0005158327,0.894753,0.03300216,0.04938594,0.01880804,0.0001374663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07135156,0.001218427,0.9181319,0.0004556,0.0002383166,0.0002723494,0.0008973111,0.001897238,0.005537228],"genre_scores_gemma":[0.8156435,0.0004590819,0.1763725,0.0002015495,0.0001183053,0.0001318552,0.0009673713,0.00005462378,0.006051147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002246924,"threshold_uncertainty_score":0.007516682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04851719953432076,"score_gpt":0.3801350198759842,"score_spread":0.3316178203416635,"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."}}