{"id":"W4303982392","doi":"10.3390/s22197655","title":"Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Canada; University of Victoria","funders":"","keywords":"Computer science; Authentication (law); Biometrics; Computer security; Context (archaeology); Counterfeit; Human–computer interaction; Artificial intelligence; Machine learning; Multimedia","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":[0.0009068892,0.00225005,0.0009922378,0.002127197,0.0004426927,0.001864464,0.0009011053,0.002078641,0.008112115],"category_scores_gemma":[0.001355561,0.000685942,0.0008852033,0.002297098,0.000910891,0.003979599,0.0009884033,0.003877934,0.006668773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008757943,"about_ca_system_score_gemma":0.0004111931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001115268,"about_ca_topic_score_gemma":0.001202123,"domain_scores_codex":[0.999527,0.0001403765,0.00003469783,0.0001117094,0.0001492262,0.00003714799],"domain_scores_gemma":[0.9993418,0.0004264337,0.00003933383,0.00002783686,0.0001242191,0.00004032371],"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":[0.0001206243,0.000526274,0.001060675,0.002688483,0.0001748798,0.0005103906,0.0004739971,0.008197316,0.003853349,0.09548783,0.3090508,0.5778552],"study_design_scores_gemma":[0.00001711301,0.0002780855,0.002488588,0.001689336,0.00004542713,0.0009787582,0.0001386732,0.01373222,0.001148898,0.05818521,0.9211923,0.0001051984],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.002509793,0.8024809,0.122486,0.00728066,0.008704602,0.0001453995,0.0002865903,0.000546417,0.05555964],"genre_scores_gemma":[0.02403934,0.7312465,0.1078681,0.007394925,0.02508754,0.0004215656,0.0007235564,0.0005070838,0.1027114],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008112115,"threshold_uncertainty_score":0.0271377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03581303710179541,"score_gpt":0.3035662580561431,"score_spread":0.2677532209543477,"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."}}