{"id":"W2191657759","doi":"10.1016/j.procs.2015.05.075","title":"Ubiquitous Tele-health System for Elderly Patients with Alzheimer's","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; Acadia University","keywords":"Wearable computer; Internet of Things; Computer science; Radio-frequency identification; Health care; Elderly people; Wireless; Ultra high frequency; Identification (biology); Wireless sensor network; Wearable technology; Telecommunications; The Internet; Ubiquitous computing; Computer security; Medical emergency; Embedded system; Medicine; World Wide Web; Human–computer interaction; Gerontology; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0015522,0.0002876093,0.0003989628,0.000336303,0.0004365898,0.000788855,0.002234143,0.00005592872,4.318632e-7],"category_scores_gemma":[0.00007155471,0.0002366571,0.00006021168,0.001474616,0.0002119047,0.002418881,0.0004878311,0.0001380783,0.00006156343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003143534,"about_ca_system_score_gemma":0.001951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005296452,"about_ca_topic_score_gemma":0.000023048,"domain_scores_codex":[0.9962199,0.00007459568,0.0004475375,0.001150081,0.001273272,0.0008345643],"domain_scores_gemma":[0.9961741,0.0001707619,0.0003981379,0.0008035193,0.001762105,0.0006913528],"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":[0.00004799594,0.0003016683,0.006948393,0.0001896431,0.00005341678,0.000006653661,0.003991817,0.0001355298,0.000019705,0.004093247,0.003831912,0.98038],"study_design_scores_gemma":[0.01651487,0.01711806,0.02619485,0.001649637,0.00009879219,0.0007156489,0.000620589,0.8876807,0.005792676,0.001877884,0.03740642,0.00432989],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02473619,0.0001450931,0.9696233,0.0007226404,0.002345358,0.001522316,0.00001150842,0.0006477154,0.0002458556],"genre_scores_gemma":[0.8707896,4.805339e-7,0.1283092,0.0004267928,0.000275371,0.000167137,0.000003937566,0.00001797868,0.000009477257],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9760501,"threshold_uncertainty_score":0.9650598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04049713077978299,"score_gpt":0.2648527621645033,"score_spread":0.2243556313847203,"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."}}