{"id":"W2161421314","doi":"10.1111/j.1467-8659.2006.00955.x","title":"A Predictive Light Transport Model for the Human Iris","year":2006,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Rendering (computer graphics); Computer graphics; Predictability; Artificial intelligence; Graphics; Procedural modeling; Computer vision; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003416604,0.000153291,0.000145771,0.0003149555,0.0005718128,0.0001708573,0.001256138,0.00009848144,0.00000150606],"category_scores_gemma":[0.000002832232,0.000115902,0.0002257488,0.001063116,0.00008848056,0.0002102637,0.0001133648,0.0001520402,0.000004448768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002051653,"about_ca_system_score_gemma":0.0000421387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004910474,"about_ca_topic_score_gemma":0.0000691963,"domain_scores_codex":[0.9986197,0.00002212468,0.0003004669,0.0004215253,0.0002973276,0.0003388152],"domain_scores_gemma":[0.9988657,0.0000892648,0.0001029333,0.0006885123,0.0001934043,0.00006020969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003297583,0.0001325643,0.001216014,0.00001245892,0.00003185136,0.000001354495,0.0003885108,0.0006627882,0.0000768415,0.9560648,0.03904172,0.002367843],"study_design_scores_gemma":[0.0002976525,0.0000435424,0.007673196,0.000004850689,0.0000143322,0.000003284706,0.000004607696,0.8882009,0.0001262825,0.06319454,0.04029472,0.0001420522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001925055,0.0002152806,0.9919573,0.004535404,0.0005271446,0.0004376593,0.00002768667,0.0001736268,0.0002008234],"genre_scores_gemma":[0.9764981,0.00001293925,0.02183332,0.0009655243,0.0001611435,0.00008233573,0.00002703613,0.00001255533,0.0004070638],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.974573,"threshold_uncertainty_score":0.4726346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205594481737193,"score_gpt":0.2441193376494329,"score_spread":0.2235598894757136,"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."}}