{"id":"W1982623151","doi":"10.1016/j.diin.2009.06.003","title":"Extraction of forensically sensitive information from windows physical memory","year":2009,"lang":"en","type":"article","venue":"Digital Investigation","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; String searching algorithm; Focus (optics); String (physics); Matching (statistics); Protocol (science); Data mining; Pattern matching; Information retrieval; Artificial intelligence","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":[0.0003901022,0.0003751513,0.0003514329,0.003362502,0.0003850561,0.0009229743,0.0004906646,0.000680153,0.001320295],"category_scores_gemma":[0.003255133,0.0002544437,0.0002033122,0.001823307,0.0003782481,0.001528769,0.0008973894,0.0005500498,0.0007264138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001410043,"about_ca_system_score_gemma":0.0003818199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00018566,"about_ca_topic_score_gemma":0.0002452766,"domain_scores_codex":[0.9996173,0.00004065677,0.00003951043,0.00005597545,0.000199546,0.00004678244],"domain_scores_gemma":[0.998229,0.0004230509,0.0003417874,0.0005927085,0.0003639031,0.00004958595],"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":[0.0005273793,0.0001043477,0.009990716,0.0004816467,0.00004936755,0.003277997,0.001381581,0.002992187,0.3813826,0.00859358,0.003003133,0.5882156],"study_design_scores_gemma":[0.00003155581,0.0003628481,0.03004866,0.0002855528,0.0001620246,0.00827236,0.001587524,0.05559532,0.8488223,0.01670809,0.03800081,0.0001228382],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5640455,0.001996731,0.4189822,0.000499895,0.0002533992,0.0002450771,0.001394771,0.003486212,0.009096136],"genre_scores_gemma":[0.8265469,0.000989037,0.1677734,0.000100471,0.00006093916,0.00007811018,0.0008791428,0.0002138288,0.003358298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003362502,"threshold_uncertainty_score":0.004416823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008646516644480599,"score_gpt":0.2095923998710739,"score_spread":0.2009458832265933,"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."}}