{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001087045,0.00008142747,0.0001917779,0.0006057082,0.00003239357,0.000009650046,0.00004727386,0.00003056239,0.001323293],"category_scores_gemma":[0.0001160598,0.00005499582,0.00008498963,0.0008519202,0.00003573285,0.00003469713,0.00001925966,0.0001106403,0.00006948908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002766131,"about_ca_system_score_gemma":0.00001271667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002197708,"about_ca_topic_score_gemma":0.000008172187,"domain_scores_codex":[0.9993393,0.00002163774,0.0001935278,0.0001473683,0.0001364211,0.000161685],"domain_scores_gemma":[0.9996449,0.00003850108,0.00002671,0.0001468555,0.00005216152,0.00009085905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002903498,0.0003666662,0.9816402,0.00003735354,0.00004111907,0.0001073181,0.0004605074,1.394648e-7,0.01395886,0.0006032849,0.0008768414,0.001878707],"study_design_scores_gemma":[0.001085874,0.0001812212,0.9688579,0.0001410022,0.0001670527,0.000050445,0.000920934,0.002880197,0.02477243,0.0003434316,0.000432439,0.0001670068],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9427172,0.00004625737,0.0005032134,0.0007985343,0.00002834895,0.00005597771,0.000001453051,0.00005181979,0.0557972],"genre_scores_gemma":[0.9956004,0.00002061581,0.001886925,0.0002276882,0.00004474405,0.000004855066,0.00001965059,0.0000076932,0.002187416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05360978,"threshold_uncertainty_score":0.9995896,"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."}}