{"id":"W2031996436","doi":"10.1145/1101389.1101411","title":"Iris synthesis","year":2005,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Iris recognition; IRIS (biosensor); Computer science; Biometrics; Heuristics; Artificial intelligence; Subdivision; Computer vision; Identification (biology); Image (mathematics); Pattern recognition (psychology); Set (abstract data type); Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030476,0.0005761242,0.0006076252,0.0005851048,0.0004132813,0.0009577341,0.0004635325,0.0005656904,0.02114628],"category_scores_gemma":[0.0009706846,0.0002899192,0.0005717147,0.0004497875,0.0002957174,0.0007085557,0.000809644,0.0005722514,0.007752782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003995733,"about_ca_system_score_gemma":0.0004058921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005320807,"about_ca_topic_score_gemma":0.0006744707,"domain_scores_codex":[0.9996277,0.00003166451,0.0000223945,0.0001165666,0.0001653964,0.00003624629],"domain_scores_gemma":[0.9995514,0.00007864167,0.00004284943,0.0001628822,0.0001342474,0.0000299337],"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":[0.0006888824,0.00006573638,0.0005754519,0.0004247866,0.00004482075,0.0002211012,0.0001119995,0.02453713,0.4310954,0.02403744,0.007970649,0.5102267],"study_design_scores_gemma":[0.0001146921,0.0005207297,0.001783393,0.00007347945,0.00008296264,0.001249058,0.0001027938,0.2356973,0.6196184,0.008803193,0.1318723,0.00008174936],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03552702,0.001439205,0.9015058,0.000273002,0.0004517683,0.0002426086,0.0007562856,0.005375616,0.05442881],"genre_scores_gemma":[0.2964918,0.001090805,0.6495215,0.0002499066,0.0001236418,0.0002344817,0.001645638,0.0006582349,0.0499841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02114628,"threshold_uncertainty_score":0.0707413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02045581066329426,"score_gpt":0.2448046679689665,"score_spread":0.2243488573056723,"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."}}