{"id":"W2118042228","doi":"10.1109/wdfia.2008.11","title":"Two-Dimensional Evidence Reliability Amplification Process Model for Digital Forensics","year":2008,"lang":"en","type":"article","venue":"","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Digital forensics; Process (computing); Reliability (semiconductor); Computer forensics; Exploit; Digital evidence; Network forensics; Intersection (aeronautics); Process modeling; Variety (cybernetics); Data science; Work in process; Computer security; Artificial intelligence; Engineering","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":[0.004728104,0.001103299,0.000907088,0.002242808,0.001182534,0.003524897,0.002999645,0.00292885,0.00548528],"category_scores_gemma":[0.01136576,0.000558473,0.001674063,0.001916873,0.003163393,0.005882736,0.002672092,0.002802872,0.001428017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002587592,"about_ca_system_score_gemma":0.002282613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003935458,"about_ca_topic_score_gemma":0.00194437,"domain_scores_codex":[0.9963723,0.001369353,0.0002567364,0.0007067911,0.00100869,0.0002860334],"domain_scores_gemma":[0.9930457,0.004097974,0.0008401854,0.0006102194,0.0011713,0.0002346987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008532353,0.000084761,0.00137143,0.0001321234,0.00003238034,0.000403735,0.0008147637,0.180753,0.001570115,0.7915967,0.00102127,0.02213458],"study_design_scores_gemma":[0.00003133083,0.00008745423,0.0002608472,0.00003856585,0.00003898831,0.0002904556,0.0001322558,0.8144484,0.001022368,0.1790624,0.004533476,0.00005345899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006716907,0.0001423198,0.9855947,0.000546785,0.00003297974,0.0001460616,0.00008880868,0.0001534703,0.006577947],"genre_scores_gemma":[0.5627587,0.0008852689,0.4221375,0.0002049786,0.0001063081,0.00108531,0.000255651,0.00006111461,0.01250517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00548528,"threshold_uncertainty_score":0.02500492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05600883287686583,"score_gpt":0.2812215195169538,"score_spread":0.225212686640088,"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."}}