{"id":"W1989627421","doi":"10.1109/iembs.2011.6090986","title":"Supervised retinal biometrics in different lighting conditions","year":2011,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Retina; Reliability (semiconductor); Retinal; Pattern recognition (psychology); Similarity (geometry); Computer vision; Construct (python library); Biometric data; Image (mathematics)","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":[0.0005578139,0.0002395674,0.0004597731,0.000576858,0.0002213547,0.0003173992,0.0002227929,0.0003987299,0.0006552903],"category_scores_gemma":[0.002402621,0.0001315575,0.0002904736,0.0004063344,0.0002949071,0.0003511887,0.0002966934,0.0002668562,0.0002454177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002482433,"about_ca_system_score_gemma":0.0001865252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007312797,"about_ca_topic_score_gemma":0.001408383,"domain_scores_codex":[0.9993203,0.0002105328,0.00003590775,0.0001764335,0.0002091753,0.00004769361],"domain_scores_gemma":[0.9984562,0.0005013153,0.0002828623,0.0003043181,0.0004061158,0.00004915864],"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":[0.001849545,0.000521052,0.038225,0.0003354285,0.000237338,0.0003941852,0.0004995911,0.04872766,0.4001653,0.0009488832,0.001344127,0.5067518],"study_design_scores_gemma":[0.00006845684,0.001109468,0.1984231,0.00003759534,0.0001701001,0.002480229,0.0002904064,0.535562,0.2585894,0.001885566,0.001280744,0.0001029403],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.940385,0.0001695776,0.05785964,0.0000474447,0.00002358424,0.00003036857,0.0001641508,0.0002287821,0.001091388],"genre_scores_gemma":[0.981925,0.00005673447,0.01747249,0.000009349459,0.00001042086,0.00001555457,0.0001265235,0.00001481162,0.0003691163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007312797,"threshold_uncertainty_score":0.002950013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05775958946539658,"score_gpt":0.2981797792092099,"score_spread":0.2404201897438133,"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."}}