{"id":"W2093197020","doi":"10.1109/tdsc.2007.70207","title":"A New Biometric Technology Based on Mouse Dynamics","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":366,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Biometrics; Computer science; Artifact (error); Confusion matrix; Word error rate; Data mining; Process (computing); Receiver operating characteristic; Set (abstract data type); False positive rate; Artificial intelligence; Crossover; Detector; Pattern recognition (psychology); Machine learning","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":[0.001015752,0.0006857445,0.0009209013,0.001452263,0.0002708549,0.0008832332,0.001067658,0.001236635,0.003336428],"category_scores_gemma":[0.003133136,0.0003169109,0.0004771079,0.001098339,0.0005690717,0.002322909,0.001319616,0.001030458,0.001654287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000302385,"about_ca_system_score_gemma":0.0002287885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002423433,"about_ca_topic_score_gemma":0.0002683272,"domain_scores_codex":[0.9981383,0.0004108177,0.0001051114,0.0004193169,0.0008450984,0.00008144033],"domain_scores_gemma":[0.9983872,0.0005126573,0.0003932012,0.0002970457,0.0003018824,0.0001079977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000800472,0.000184267,0.01008844,0.001089292,0.0001442422,0.0004208965,0.000285001,0.003340505,0.5160074,0.01766711,0.008514796,0.4414576],"study_design_scores_gemma":[0.000315658,0.005459506,0.05871629,0.0009651408,0.0006507385,0.02037046,0.0002611733,0.2307784,0.4224356,0.01692582,0.2422626,0.0008586382],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05040943,0.006571529,0.9291869,0.0008574338,0.001104598,0.0001988217,0.0008298567,0.003582515,0.007258988],"genre_scores_gemma":[0.5466178,0.005323654,0.4295665,0.00201535,0.000795697,0.0005140174,0.0007616976,0.0002164347,0.01418891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003336428,"threshold_uncertainty_score":0.01116145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01029130423544031,"score_gpt":0.2408290485271312,"score_spread":0.2305377442916909,"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."}}