{"id":"W4328054402","doi":"10.18280/ts.400109","title":"Finger Vein Recognition Based on Multi-Features Fusion","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fusion; Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Speech recognition","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":[0.0005211146,0.0001015778,0.00008252311,0.0005320802,0.0001543401,0.0001599939,0.000338684,0.0000590079,0.0003322099],"category_scores_gemma":[0.00004059449,0.00009287019,0.000065184,0.001394269,0.00001944646,0.0001576129,0.00005076646,0.0001043841,0.0007855133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003836554,"about_ca_system_score_gemma":0.00002985384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001535792,"about_ca_topic_score_gemma":0.000006815327,"domain_scores_codex":[0.9987994,0.00008401922,0.0001792101,0.0003220215,0.0004161227,0.0001992013],"domain_scores_gemma":[0.9994643,0.0001022864,0.00006111077,0.0002288416,0.00007148361,0.00007196172],"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":[0.00006980546,0.001289706,0.0005261809,0.00007521731,0.00003092864,0.00005836443,0.00170318,0.0007420313,0.02677938,0.003691602,0.1136712,0.8513624],"study_design_scores_gemma":[0.001955909,0.0002738163,0.1465739,0.00006329661,0.00001097692,0.000002511598,0.00004477237,0.8016708,0.01589986,0.0006866226,0.0324007,0.0004168524],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1601536,0.00005685013,0.8256684,0.008063762,0.001528166,0.0007598448,0.00006902633,0.00143999,0.002260327],"genre_scores_gemma":[0.9906678,0.000007213025,0.00731991,0.001151018,0.00008434651,0.00003578869,0.0001404443,0.000007635578,0.0005858278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8509455,"threshold_uncertainty_score":0.9999925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05554437092001527,"score_gpt":0.2713129360742676,"score_spread":0.2157685651542523,"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."}}