{"id":"W2039401290","doi":"10.1109/sadfe.2010.20","title":"Digital Records Forensics: Ensuring Authenticity and Trustworthiness of Evidence Over Time","year":2010,"lang":"en","type":"article","venue":"","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Digital forensics; Workflow; Digital evidence; Trustworthiness; Computer forensics; Computer science; Domain (mathematical analysis); Data science; Work (physics); Network forensics; Computer security; World Wide Web; Internet privacy; Engineering; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001415189,0.0001127762,0.0001561404,0.00004288814,0.00003755526,0.0002746711,0.0003731915,0.00004736695,0.00002951889],"category_scores_gemma":[0.0001157093,0.00007784976,0.0000520567,0.0001787788,0.0001491015,0.001803502,0.0004050698,0.0001090629,0.00002491527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004199636,"about_ca_system_score_gemma":0.00002896548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002457473,"about_ca_topic_score_gemma":0.00002554746,"domain_scores_codex":[0.9991377,0.00000556074,0.0001962849,0.0002583997,0.0002237173,0.0001783286],"domain_scores_gemma":[0.9992269,0.0001325947,0.00007183192,0.0003861311,0.00009122903,0.00009128448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001111363,0.00005631104,0.03893455,0.00003561139,0.00002329403,0.00001062474,0.0003765775,0.000002839214,0.001546949,0.09367029,0.0007456305,0.8645862],"study_design_scores_gemma":[0.001373594,0.0006017451,0.4431659,0.0004675167,0.00005258617,0.0002869515,0.00004954018,0.1374917,0.03894942,0.3655346,0.01011644,0.001910085],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9560718,0.00001563693,0.01902243,0.0001678518,0.0003950046,0.00007365424,0.000004997819,0.0001084995,0.02414011],"genre_scores_gemma":[0.9889434,0.000002560806,0.006532184,0.00005782075,0.00004195641,0.000001316201,9.110038e-7,0.000005861662,0.004414017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8626761,"threshold_uncertainty_score":0.3174621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133203309839773,"score_gpt":0.2348229164674742,"score_spread":0.2134908833690765,"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."}}