{"id":"W7116787370","doi":"10.1109/mitp.2025.3571254","title":"Cybersecurity Challenges for the Elderly: Vulnerabilities and Risks","year":2025,"lang":"","type":"article","venue":"IT Professional","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Data breach; Information security; Risk management; Cybercrime; Vulnerability (computing); Government (linguistics); Security information and event management","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":[0.002761874,0.0002477674,0.0002726413,0.001155346,0.00234927,0.002710539,0.0002787282,0.001208791,0.003047946],"category_scores_gemma":[0.01027203,0.000169159,0.00034796,0.0006327973,0.001399388,0.003391598,0.00288466,0.001367205,0.00034503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008102402,"about_ca_system_score_gemma":0.002227678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004246228,"about_ca_topic_score_gemma":0.007936469,"domain_scores_codex":[0.9988939,0.0003902389,0.00008936315,0.00007624837,0.0002905008,0.000259793],"domain_scores_gemma":[0.9950377,0.001568115,0.001157114,0.0002050438,0.0008352509,0.001196635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001413518,0.0004178772,0.6591191,0.0004065872,0.0001519579,0.003754634,0.08484834,0.0004032168,0.001021318,0.01376185,0.01562899,0.2203447],"study_design_scores_gemma":[0.00002617138,0.001071888,0.425842,0.003609658,0.0002356672,0.01406663,0.4138386,0.001594511,0.0009515437,0.04908467,0.08954714,0.0001315074],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9454597,0.008165635,0.001219356,0.02716057,0.0002173204,0.00003459163,0.0001002103,0.00002327543,0.0176194],"genre_scores_gemma":[0.9923283,0.004476108,0.0004646035,0.001412766,0.0001035382,0.00001094149,0.00002525447,0.000003565276,0.001174849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004246228,"threshold_uncertainty_score":0.01460636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06405224288180737,"score_gpt":0.3844564134751084,"score_spread":0.320404170593301,"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."}}