{"id":"W2775835662","doi":"","title":"IC Decamouaging:: Reverse Engineering Camouflaged ICs within Minutes.","year":2015,"lang":"en","type":"article","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reverse engineering; Engineering; Forensic engineering; Computer science; Operating system","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":[0.0004863618,0.001304072,0.0004720525,0.0008436321,0.0006759691,0.001403382,0.001289199,0.001175761,0.02998034],"category_scores_gemma":[0.002359227,0.0003868926,0.0004399824,0.0004189594,0.0007659037,0.001423674,0.001888879,0.001154716,0.008122319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003942718,"about_ca_system_score_gemma":0.000717593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001067336,"about_ca_topic_score_gemma":0.002876046,"domain_scores_codex":[0.9994716,0.00004153832,0.00001692312,0.00007863877,0.0002953681,0.00009599171],"domain_scores_gemma":[0.9990429,0.0001689376,0.00005644855,0.00044075,0.0002469348,0.00004406392],"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":[0.0006321764,0.0001565892,0.001740647,0.0007048111,0.00008054835,0.001443271,0.0007297782,0.008067212,0.2456126,0.01714481,0.0679998,0.6556878],"study_design_scores_gemma":[0.00008745035,0.000660528,0.002156153,0.0002518058,0.0001113704,0.002809641,0.0006624047,0.04522907,0.5851252,0.009706445,0.3530856,0.0001143144],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1530547,0.005485169,0.5908358,0.002396691,0.004757252,0.0009858287,0.001775507,0.0412941,0.1994151],"genre_scores_gemma":[0.6093628,0.001786292,0.2643942,0.00164445,0.0002848047,0.0002545162,0.00149816,0.004818985,0.1159557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02998034,"threshold_uncertainty_score":0.1002942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02502063523506517,"score_gpt":0.249010107942898,"score_spread":0.2239894727078329,"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."}}