{"id":"W2131282907","doi":"10.1109/tip.2009.2033427","title":"An Analysis of IrisCode","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hamming distance; Iris recognition; Bitwise operation; Pattern recognition (psychology); Biometrics; Artificial intelligence; Coding (social sciences); Computer science; Mathematics; Cluster analysis; Gabor filter; Hamming code; Algorithm; Feature extraction; Decoding methods; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001990357,0.00008134627,0.0001544815,0.001122709,0.0001591923,0.0002117127,0.0004384557,0.00004278416,0.00002829526],"category_scores_gemma":[0.000003334558,0.00007944167,0.00010332,0.004391575,0.00003996728,0.0009186176,4.541822e-7,0.0001034779,0.000008275947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000263842,"about_ca_system_score_gemma":0.00004953581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001443637,"about_ca_topic_score_gemma":0.000007737292,"domain_scores_codex":[0.9990818,0.00003845658,0.0002282135,0.0002756219,0.0002465382,0.0001293792],"domain_scores_gemma":[0.999254,0.00001886434,0.00009568968,0.0004035876,0.0001590045,0.00006887108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007964573,0.0007533936,0.00002018158,0.00001677068,0.00006682175,0.00000262104,0.001193109,0.003213553,0.0474408,0.000277234,0.00002074053,0.9469868],"study_design_scores_gemma":[0.0001590009,0.00008211469,0.004676978,0.00001084386,0.0001730042,0.000002383179,0.00006476604,0.8618365,0.1324811,0.0002859197,0.00007119917,0.0001561633],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01092322,0.00003910609,0.9881871,0.0003085242,0.00007275055,0.0000431578,0.000006397978,0.0001221445,0.0002976468],"genre_scores_gemma":[0.9360331,0.00000777396,0.06372175,0.0001591453,0.000005883574,0.000002319117,0.000001630318,0.000002705804,0.00006563862],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9468306,"threshold_uncertainty_score":0.3239537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922595505371614,"score_gpt":0.3032656252028255,"score_spread":0.2840396701491094,"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."}}