{"id":"W3143652836","doi":"10.1109/ultsym.2009.5441399","title":"High resolution ultrasonic method for 3D fingerprint recognizable characteristics in biometrics identification","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Biometrics; Fingerprint (computing); Identification (biology); Computer science; Artificial intelligence; Visualization; Computer vision; Fingerprint recognition; Ultrasonic sensor; Crime scene; Usability; Pattern recognition (psychology); Human–computer interaction; Geography; Acoustics","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":[0.002114266,0.0001244124,0.0002034898,0.001602065,0.0001199712,0.0003361282,0.0006307988,0.0001328188,0.00002328551],"category_scores_gemma":[0.0009635182,0.0001275114,0.0000630606,0.005639489,0.00001498102,0.0004643013,0.00004571324,0.00011805,0.00006298874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001795774,"about_ca_system_score_gemma":0.00006752781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009801884,"about_ca_topic_score_gemma":0.0000116583,"domain_scores_codex":[0.9982026,0.00009846458,0.0005958628,0.0005260071,0.0002701831,0.0003069424],"domain_scores_gemma":[0.9985567,0.000285159,0.0002341412,0.0005665613,0.0002798985,0.00007752834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001196066,0.0003324367,0.0002150972,0.00002289994,0.000006785453,0.000001145693,0.0002004519,0.00001440878,0.009003426,0.1044904,0.001706251,0.8839947],"study_design_scores_gemma":[0.0009535807,0.000174259,0.2317947,0.00002524012,0.00001622818,0.00001113132,0.00002808598,0.6913151,0.02084988,0.02244977,0.03183546,0.000546523],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004518915,0.00007151012,0.9919636,0.002038699,0.0006649104,0.0003830506,0.00001478206,0.0001595467,0.0001849733],"genre_scores_gemma":[0.3568558,0.00008458449,0.6418586,0.0003367478,0.00004424643,0.00002578513,0.00006248538,0.000005024731,0.0007266664],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8834482,"threshold_uncertainty_score":0.5199765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0302804846346709,"score_gpt":0.300721366483908,"score_spread":0.270440881849237,"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."}}