{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004538995,0.0000965994,0.0001548222,0.0006648921,0.0001391325,0.0001375984,0.001092295,0.00006819377,0.0001324544],"category_scores_gemma":[0.002600505,0.00006085089,0.00008323004,0.00147611,0.0002657046,0.0002088948,0.0003243239,0.0001807749,0.0009967361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006883712,"about_ca_system_score_gemma":0.00004520925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006272866,"about_ca_topic_score_gemma":0.000003985347,"domain_scores_codex":[0.997424,0.0006191733,0.000336631,0.0004210508,0.0007969313,0.0004022006],"domain_scores_gemma":[0.9951308,0.003687365,0.00005745005,0.0007619891,0.0002565974,0.0001057567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003764945,0.00006460983,0.00008159786,0.00001874165,0.000006638902,0.00000380352,0.0002439161,0.000004455404,0.04612048,0.08435076,0.0001270722,0.8689403],"study_design_scores_gemma":[0.00001919795,0.0005933946,0.0001607375,0.00005799358,9.478085e-7,0.000001230287,0.00001620384,0.005210533,0.9583321,0.03489535,0.00063076,0.00008157396],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4060638,0.00004297508,0.5906239,0.00153584,0.0003659495,0.0002761605,0.000003063315,0.00007103202,0.001017352],"genre_scores_gemma":[0.997918,0.00004207993,0.001716874,0.0000170551,0.00004774606,0.00002555756,3.246739e-7,0.000005896919,0.0002265069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9122116,"threshold_uncertainty_score":0.9997811,"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."}}