{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001081795,0.0004287607,0.0003129957,0.0003886894,0.0002238847,0.000581226,0.0002030731,0.0003480554,0.003473801],"category_scores_gemma":[0.007646369,0.0001602199,0.0004559896,0.0004160354,0.0002493842,0.0005950593,0.0003357183,0.0003579306,0.0006760084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001843676,"about_ca_system_score_gemma":0.0002290671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001239504,"about_ca_topic_score_gemma":0.001207687,"domain_scores_codex":[0.9988551,0.0002539762,0.00006621928,0.0002113808,0.0004527542,0.0001605889],"domain_scores_gemma":[0.9894657,0.00750071,0.0007629608,0.0005209077,0.001520168,0.0002295575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005280108,0.001084602,0.6755691,0.0005283349,0.0004351209,0.0009926782,0.0007355421,0.007440709,0.1377098,0.0003245664,0.001534975,0.1683645],"study_design_scores_gemma":[0.00001875204,0.003576788,0.900786,0.00006573254,0.0004811355,0.001293326,0.001036292,0.005304517,0.08532759,0.0002266611,0.001832624,0.00005062829],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995147,0.0007867839,0.001665218,0.00007364519,0.00002615199,0.00002635401,0.0003488805,0.00003069201,0.00189528],"genre_scores_gemma":[0.9966651,0.0005654738,0.0009552068,0.00005266138,0.00001535582,0.00001656685,0.0002662542,0.00001448047,0.001448882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003473801,"threshold_uncertainty_score":0.011621,"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."}}