{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004519497,0.0001205467,0.0001426288,0.00006942363,0.0000359456,0.0003422678,0.0001731806,0.00005012055,6.873466e-7],"category_scores_gemma":[0.00009641072,0.0001122417,0.00007379198,0.0002715561,0.0001105733,0.009061589,0.00004359775,0.00008364666,0.0001073632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002960957,"about_ca_system_score_gemma":0.00005042417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001580385,"about_ca_topic_score_gemma":7.365643e-7,"domain_scores_codex":[0.9991057,0.00001310784,0.0002594735,0.0001625394,0.000326268,0.0001329125],"domain_scores_gemma":[0.9992175,0.00006022693,0.0001869487,0.0002295705,0.0002118048,0.00009401844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002076846,0.0001102494,0.0001485237,0.000006885626,0.00002068846,0.000004574688,0.002785172,0.0002613194,0.008315785,0.1847371,0.001370657,0.8022183],"study_design_scores_gemma":[0.0004820503,0.0003545578,0.04047807,0.00006196179,0.00001321314,0.00001441415,0.0001738406,0.06384913,0.1791981,0.7142823,0.0007466025,0.0003458064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8624646,0.000003561852,0.1001731,0.0006020105,0.0001924112,0.0001449132,0.00003896959,0.0001577489,0.03622266],"genre_scores_gemma":[0.9960901,4.462682e-7,0.003038055,0.0005326332,0.00007843127,0.000001928683,0.0002064864,0.000002987195,0.00004895946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8018725,"threshold_uncertainty_score":0.6569433,"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."}}