{"id":"W2944328653","doi":"10.22215/etd/2018-13180","title":"Effects of Sensors, Age, and Gender on Fingerprint Image Quality","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Fingerprint (computing); Fingerprint recognition; Quality (philosophy); USable; Biometrics; Vendor; Artificial intelligence; Pattern recognition (psychology); Computer science; Image quality; Multispectral image; Computer vision; Engineering; Image (mathematics); Multimedia","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":[0.0003810374,0.0001441757,0.0002289042,0.0003413166,0.00005553273,0.0001187468,0.0003627891,0.0001701563,0.00003169245],"category_scores_gemma":[0.0002163472,0.0001262457,0.00006751388,0.0004411152,0.00004273884,0.00008967076,0.00005712944,0.0001285487,0.00005518606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001899553,"about_ca_system_score_gemma":0.00003855348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009200054,"about_ca_topic_score_gemma":0.00002644065,"domain_scores_codex":[0.9987262,0.0001135608,0.0002840684,0.0004216839,0.0003320725,0.0001223493],"domain_scores_gemma":[0.9988923,0.0002003907,0.0002160746,0.0004521993,0.0001764325,0.0000626123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001540731,0.001531095,0.0001576243,0.01065513,0.0003671227,0.0001033951,0.02867703,3.438015e-7,0.09317859,0.5971922,0.03718697,0.2307964],"study_design_scores_gemma":[0.000767337,0.0001754228,0.6251049,0.0001255453,0.00004823399,0.000003136411,0.00019901,0.0009290753,0.351505,0.01359175,0.006726137,0.0008244729],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8998193,0.000287412,0.05235798,0.00009136312,0.001971941,0.0005682429,0.000008853875,0.0001913899,0.04470348],"genre_scores_gemma":[0.9382144,0.0001450664,0.04173749,0.0002145163,0.00008634239,0.00001791359,0.0001247594,0.00001855327,0.01944095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6249472,"threshold_uncertainty_score":0.5148152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03320760046724055,"score_gpt":0.33354582871753,"score_spread":0.3003382282502894,"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."}}