{"id":"W2295254979","doi":"10.5430/air.v5n1p160","title":"Effect of parameter values on fingerprint filtering","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normalization (sociology); Artificial intelligence; Fingerprint (computing); Pattern recognition (psychology); Biometrics; Computer science; Palm print; Gabor filter; Consistency (knowledge bases); Segmentation; Noise (video); Filter (signal processing); Feature extraction; Computer vision; Mathematics; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001555803,0.0009446442,0.0006128527,0.001125827,0.0003585471,0.0007815235,0.000734485,0.001064137,0.001216897],"category_scores_gemma":[0.01687148,0.0002486162,0.0004395131,0.001109293,0.0004037414,0.001013137,0.0003166361,0.0004151106,0.0003429721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003913767,"about_ca_system_score_gemma":0.0002947539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009542406,"about_ca_topic_score_gemma":0.0005101842,"domain_scores_codex":[0.9982304,0.0004782872,0.0002813084,0.0003737871,0.0004813388,0.0001548985],"domain_scores_gemma":[0.9879093,0.008041907,0.0007696533,0.00142003,0.001746647,0.0001125607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005329893,0.0008620258,0.0208088,0.001582344,0.000417663,0.001414198,0.000804007,0.1227802,0.4243441,0.001863629,0.001682145,0.418111],"study_design_scores_gemma":[0.0001425403,0.003296614,0.03462446,0.0003110595,0.001015546,0.00268103,0.0006268958,0.1776245,0.7707521,0.001193344,0.007515341,0.000216569],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.899592,0.004435822,0.08993199,0.0002171094,0.000273588,0.0001754514,0.0002507281,0.001130652,0.003992796],"genre_scores_gemma":[0.9750586,0.0005955637,0.023676,0.00003721652,0.00001687103,0.00004818844,0.0001307844,0.00007060217,0.0003662089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001555803,"threshold_uncertainty_score":0.008227944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1913503918856039,"score_gpt":0.4467651659650407,"score_spread":0.2554147740794368,"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."}}