{"id":"W4248002105","doi":"10.22215/etd/2019-13751","title":"Biometric Quality and its Impact on Template Ageing in a Longitudinal Fingerprint Study","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Fingerprint (computing); Computer science; Quality (philosophy); Resampling; Artificial intelligence; Statistics; Data mining; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001419943,0.0002792216,0.0004397754,0.004297818,0.0000836315,0.000442576,0.0006713725,0.0001940413,0.00005555093],"category_scores_gemma":[0.0002599388,0.0002286767,0.00009851986,0.005738446,0.000006801792,0.0002810986,0.0001200537,0.0003636829,0.0001504432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001611451,"about_ca_system_score_gemma":0.0001138208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001390224,"about_ca_topic_score_gemma":0.0003776959,"domain_scores_codex":[0.9975391,0.000191355,0.0005627142,0.0008495707,0.0005825426,0.0002747295],"domain_scores_gemma":[0.9985886,0.0002777686,0.0002994068,0.0005913817,0.0001423842,0.0001005013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000616713,0.01003135,0.4899679,0.001926222,0.0008248871,0.000498783,0.04171902,0.00009418546,0.002322395,0.03401249,0.0008458691,0.4171402],"study_design_scores_gemma":[0.0004227804,0.0001953735,0.9953994,0.00003963209,0.000007004008,0.000001829926,0.0002473845,0.003101264,0.0001829514,0.00008323391,0.00003422603,0.0002849896],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945546,0.0003231726,0.001394288,0.00004716375,0.0006853028,0.0006846894,0.000006700679,0.00007771626,0.002226369],"genre_scores_gemma":[0.9971298,0.00003901115,0.0001667237,0.0000266374,0.00001460175,0.00001923263,0.00004389343,0.00001063599,0.002549457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5054314,"threshold_uncertainty_score":0.9325166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08511586875190842,"score_gpt":0.4089081374064014,"score_spread":0.323792268654493,"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."}}