{"id":"W2894269546","doi":"10.1049/iet-bmt.2018.5105","title":"Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset","year":2018,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Shared Services Canada; Public Health Agency of Canada","funders":"National Institute of Standards and Technology; Defence Research and Development Canada","keywords":"Nexus (standard); Computer science; Agency (philosophy); Interactive kiosk; Iris recognition; Data science; Artificial intelligence; World Wide Web; Biometrics; Social science; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002003213,0.0005176332,0.0004253492,0.0009375871,0.0002991984,0.0007486052,0.0002967083,0.0003357939,0.001195246],"category_scores_gemma":[0.004399643,0.00006637885,0.0004057615,0.000768695,0.0002713783,0.0005938892,0.0005911607,0.0004642712,0.00121039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004657675,"about_ca_system_score_gemma":0.0004651634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0133796,"about_ca_topic_score_gemma":0.0203799,"domain_scores_codex":[0.9986709,0.0003159952,0.000121357,0.0003181169,0.0004132563,0.0001604749],"domain_scores_gemma":[0.997163,0.00103012,0.0003217027,0.0005384748,0.0007798415,0.000166907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002240557,0.0009446572,0.6616458,0.0004579553,0.0005289915,0.0003344819,0.0004914985,0.0210419,0.01622117,0.0008259945,0.04818498,0.2470821],"study_design_scores_gemma":[0.00001921751,0.0007320713,0.9311635,0.00004239401,0.000115072,0.0003652232,0.0006427907,0.04529133,0.007750154,0.0002682977,0.01355485,0.00005505192],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761102,0.0006100984,0.002340719,0.0002496262,0.0001108565,0.00006080598,0.01684149,0.0004076946,0.003268457],"genre_scores_gemma":[0.9463289,0.0002481957,0.00324772,0.0000509101,0.00005385158,0.00004654416,0.04788034,0.000042132,0.002101444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0133796,"threshold_uncertainty_score":0.02660346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06918477058182675,"score_gpt":0.3102492522491896,"score_spread":0.2410644816673628,"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."}}