{"id":"W2790737570","doi":"10.5539/mas.v12n4p49","title":"A Novel Multi-Level Security Technique Based on IRIS Image Encoding","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Encryption; Encoding (memory); IRIS (biosensor); Authentication (law); Mobile phone; Image (mathematics); Iris recognition; Access control; Computer security; Plaintext; Computer hardware; Embedded system; Computer vision; Artificial intelligence; Operating system; Biometrics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001968807,0.00019585,0.0001627916,0.0008356429,0.0007501623,0.0005908423,0.002606708,0.00009235196,0.00002369161],"category_scores_gemma":[0.000158215,0.00018428,0.00005340444,0.003903729,0.0009612688,0.0005167313,0.0004404397,0.0002361065,0.0002095518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001787451,"about_ca_system_score_gemma":0.000320367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003300584,"about_ca_topic_score_gemma":0.000009827259,"domain_scores_codex":[0.9971239,0.00002322505,0.0002715691,0.001067107,0.0009769852,0.0005372142],"domain_scores_gemma":[0.998046,0.00007088729,0.0001371788,0.001220603,0.000303747,0.0002215894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005413417,0.0002488891,0.00001330268,0.00000742483,0.000001108877,0.000001352603,0.0008550517,0.000005608815,0.9697009,0.01777385,0.000176773,0.0112103],"study_design_scores_gemma":[0.0003033581,0.00003156406,0.001171537,0.000009161228,0.000001425035,0.000004228134,0.00001200447,0.7762787,0.2188882,0.00232516,0.0007481283,0.0002264754],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001238358,0.000003768914,0.9875053,0.0003747236,0.0002461169,0.0004215884,0.00001412619,0.0003114417,0.00988453],"genre_scores_gemma":[0.6544714,5.279206e-7,0.3447517,0.0006292563,0.00003167718,0.00005418399,0.000001077609,0.00000616,0.00005401209],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7762731,"threshold_uncertainty_score":0.7514721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05714946457292761,"score_gpt":0.296060192887671,"score_spread":0.2389107283147434,"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."}}