{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001325107,0.00002776265,0.00003321617,0.0001159603,0.00003825183,0.00008907023,0.0003810658,0.00001879848,0.0003595829],"category_scores_gemma":[0.00004473523,0.00002314244,0.00002190389,0.0005247351,0.000009177183,0.0002164359,0.00005189857,0.00002248366,0.001406378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001345121,"about_ca_system_score_gemma":0.0000104486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001274573,"about_ca_topic_score_gemma":0.000004143599,"domain_scores_codex":[0.9996203,0.00001304593,0.00006899771,0.0001176386,0.0001054316,0.00007464299],"domain_scores_gemma":[0.9996136,0.00004400823,0.00001399471,0.0002656166,0.00002501578,0.00003771574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[1.845001e-7,0.00004530129,0.0001166437,0.000001179409,0.000002972426,4.595397e-7,0.0001036217,9.461609e-7,0.0002536149,0.2051814,0.04742593,0.7468678],"study_design_scores_gemma":[0.00003676162,0.000002628718,0.004518803,7.416482e-7,9.991543e-7,0.000003484294,0.000006429577,0.02544635,0.01255777,0.0004907963,0.956861,0.00007422589],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002042807,0.0000574835,0.9361185,0.01018158,0.0001106446,0.00002684227,3.812022e-7,0.0002008339,0.05126088],"genre_scores_gemma":[0.8504671,0.000009456096,0.1437823,0.0007885349,0.0000356163,0.000003761563,1.587566e-7,0.000001172572,0.004911868],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9094351,"threshold_uncertainty_score":0.9993712,"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."}}