{"id":"W1998448825","doi":"10.1145/2076732.2076756","title":"Dynamic sample size detection in continuous authentication using sequential sampling","year":2011,"lang":"en","type":"article","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Authentication (law); Session (web analytics); Biometrics; Process (computing); Sampling (signal processing); Sample (material); Scheme (mathematics); Data mining; Real-time computing; Artificial intelligence; Computer security; Computer vision; Operating system; Mathematics","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":[0.0003528394,0.00009996534,0.0001287645,0.000143026,0.0000832034,0.0001061676,0.000378354,0.0000651661,0.00007332404],"category_scores_gemma":[0.0001329078,0.0001014624,0.00004764468,0.0003566835,0.00002374935,0.0004207039,0.00008409657,0.00008470653,0.00004382709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009904995,"about_ca_system_score_gemma":0.00003505993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001458206,"about_ca_topic_score_gemma":0.0006681873,"domain_scores_codex":[0.9988905,0.00008567571,0.0003287765,0.0003104817,0.0001661633,0.000218411],"domain_scores_gemma":[0.9992631,0.0001141964,0.000102142,0.0003956196,0.00006705677,0.00005789391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007249363,0.001219211,0.02899979,0.0001769968,0.0001218122,0.00001962388,0.381301,0.00005809823,0.3964093,0.1015828,0.00001126999,0.09002758],"study_design_scores_gemma":[0.0003620185,0.0000310495,0.02054394,0.00002226801,0.000008312862,0.00002092719,0.0003154593,0.9547509,0.008420981,0.01519306,0.000112672,0.0002184262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4131559,0.000007491235,0.5860779,0.00002609051,0.0002895153,0.000136942,7.654212e-7,0.0001149262,0.0001905195],"genre_scores_gemma":[0.9368232,0.000001565752,0.06301936,0.00005220751,0.00001195325,0.000009958185,0.000001098087,0.000007550394,0.00007307308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9546928,"threshold_uncertainty_score":0.4137516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05959039912421074,"score_gpt":0.2867051263590897,"score_spread":0.2271147272348789,"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."}}