{"id":"W2741205913","doi":"","title":"Touchscreen Biometrics Across Multiple Devices.","year":2017,"lang":"en","type":"article","venue":"Symposium On Usable Privacy and Security","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Touchscreen; Biometrics; Computer science; Human–computer interaction; Computer security","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":[0.001049987,0.0006427945,0.0008650783,0.001049857,0.0005798656,0.001691773,0.0009137427,0.001477478,0.008894344],"category_scores_gemma":[0.003046899,0.0005104367,0.0004229123,0.00128963,0.0004493675,0.003552513,0.002540457,0.001263065,0.003255715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003638272,"about_ca_system_score_gemma":0.0003138447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007894088,"about_ca_topic_score_gemma":0.001168615,"domain_scores_codex":[0.9981418,0.0003185307,0.0001409392,0.0004399488,0.0007158198,0.0002429831],"domain_scores_gemma":[0.9982201,0.0002844722,0.0002096906,0.0007368549,0.0004465395,0.0001022994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00119437,0.0001646707,0.007452208,0.0004818797,0.0002160035,0.001024882,0.0004041521,0.003468409,0.2194409,0.01962594,0.01512202,0.7314045],"study_design_scores_gemma":[0.0001309815,0.002361827,0.06126972,0.001210666,0.0005434747,0.01131392,0.001788284,0.1570369,0.50471,0.0302611,0.2289541,0.0004189773],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.272484,0.0368218,0.6148431,0.002892926,0.003997883,0.0003129127,0.001624744,0.005146822,0.0618758],"genre_scores_gemma":[0.922987,0.004356839,0.0431723,0.0004549246,0.000397107,0.0000771263,0.0004030226,0.00009336271,0.02805835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008894344,"threshold_uncertainty_score":0.02975458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03297082159702916,"score_gpt":0.2988049367062409,"score_spread":0.2658341151092117,"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."}}