{"id":"W2075023189","doi":"10.1002/sec.227","title":"Medical biometrics in mobile health monitoring","year":2010,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Computer science; Biometrics; Anonymity; Dependency (UML); Field (mathematics); Computer security; Protocol (science); Mobile device; Matching (statistics); Feature (linguistics); Human–computer interaction; Artificial intelligence; World Wide Web","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":[0.0008950175,0.0000709509,0.0001988478,0.0001822786,0.0001295196,0.00002196795,0.0001473944,0.0001584287,0.00002730517],"category_scores_gemma":[0.0001114956,0.00006728728,0.00003562405,0.0007008375,0.00008175861,0.00004639047,0.00009932006,0.0009177847,0.000002146095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002330797,"about_ca_system_score_gemma":0.00005336139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002771214,"about_ca_topic_score_gemma":0.0002120343,"domain_scores_codex":[0.9992017,0.00008169981,0.0002327961,0.0001264335,0.0001940664,0.0001632788],"domain_scores_gemma":[0.9990734,0.0001576676,0.00006338691,0.0004645034,0.00004436261,0.00019671],"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":[0.00002075568,0.0003756616,0.4889175,0.0000562401,0.00003299258,0.000005505329,0.001271902,0.0000424521,0.00004032732,0.0005488129,0.0001758684,0.508512],"study_design_scores_gemma":[0.007249284,0.001317144,0.4175425,0.002801389,0.0001894816,0.0002414579,0.008068752,0.4409027,0.0005171573,0.003874406,0.1160751,0.001220511],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797425,0.017076,0.0001938721,0.002001883,0.0001996355,0.0001598011,6.534444e-7,0.00005579432,0.0005698262],"genre_scores_gemma":[0.9786554,0.0201571,0.0007469588,0.0001327854,0.0002217372,0.00003453963,0.00001899176,0.000007110032,0.00002538973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5072915,"threshold_uncertainty_score":0.3987369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01301593942982866,"score_gpt":0.3252189151725061,"score_spread":0.3122029757426775,"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."}}