{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005464733,0.00006942044,0.0001093995,0.0001739191,0.0002636047,0.00005282624,0.0003024574,0.00002258339,0.00001128495],"category_scores_gemma":[0.00006514883,0.00007829047,0.00004315707,0.0003565881,0.00001042454,0.00008381274,0.0001016466,0.0001558272,0.000006572308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001801957,"about_ca_system_score_gemma":0.00006174836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002107462,"about_ca_topic_score_gemma":0.00006640777,"domain_scores_codex":[0.9988998,0.0002093956,0.0002340749,0.0002493167,0.0002091113,0.000198313],"domain_scores_gemma":[0.999519,0.0000827374,0.0001010398,0.000225721,0.00003046155,0.00004102301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003091083,0.00132033,0.02570106,0.0005128859,0.000109285,0.00004636437,0.6742151,0.05932745,0.03132859,0.1822555,0.0002248591,0.02464956],"study_design_scores_gemma":[0.0004761812,0.0000412016,0.0005429473,0.000004209272,0.000002538685,0.00001154233,0.0002187206,0.9872672,0.00003998254,0.001119982,0.01018982,0.00008564976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754075,0.00005988616,0.02195749,0.0008391465,0.001177476,0.00041538,0.000005241152,0.0001059608,0.00003194761],"genre_scores_gemma":[0.997218,0.000001600614,0.002254732,0.00005622895,0.00004460834,0.00003856316,0.00001324612,0.00000914854,0.000363889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9279398,"threshold_uncertainty_score":0.3192593,"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."}}