{"id":"W4246395638","doi":"10.32920/ryerson.14646801","title":"Sequential subspace estimator for an efficient multibiometrics authentication and encryption","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Biometrics; Subspace topology; Encryption; Authentication (law); Data mining; Cryptography; Noise (video); Fingerprint (computing); Pattern recognition (psychology); Computer security; Algorithm; Artificial intelligence; Image (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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008924153,0.0002144987,0.0002465904,0.001122941,0.0001948105,0.001388532,0.0006958175,0.0003077491,0.00001551548],"category_scores_gemma":[0.0003223006,0.0002189035,0.0001110942,0.001507168,0.00006145037,0.000248083,0.0007691861,0.0001955057,0.000008403053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001206644,"about_ca_system_score_gemma":0.000202608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001417715,"about_ca_topic_score_gemma":0.00002225774,"domain_scores_codex":[0.9978493,0.0001092626,0.0003724988,0.001012469,0.0004156642,0.0002407863],"domain_scores_gemma":[0.9978905,0.0001252525,0.0002473767,0.0009890156,0.0005573067,0.0001905918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005431539,0.004220721,0.001027738,0.002812461,0.0003818039,0.00001425952,0.01606266,0.0036122,0.04110156,0.6059082,0.003136497,0.3216676],"study_design_scores_gemma":[0.0002958531,0.00003619753,0.004046209,0.00002014863,0.00003341385,0.000006743549,0.0001075009,0.9894628,0.0040745,0.00088338,0.0007337843,0.0002995028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1182475,0.0003211081,0.8786155,0.0004960446,0.001378592,0.0006442708,0.00002447601,0.0002144436,0.00005807682],"genre_scores_gemma":[0.6657556,0.00004021563,0.333593,0.00004833359,0.00005245666,0.00005626856,0.0002615901,0.00001018001,0.0001823382],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9858506,"threshold_uncertainty_score":0.9996481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0719615744055088,"score_gpt":0.3346970098399241,"score_spread":0.2627354354344154,"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."}}