{"id":"W4361267500","doi":"10.18280/ijsse.130102","title":"Mobile Forensic Tools for Digital Crime Investigation: Comparison and Evaluation","year":2023,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forensic science; Digital forensics; Computer science; Computer security; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074081,0.001095141,0.0009967706,0.01268918,0.0007104283,0.001905308,0.001029149,0.001214638,0.002451154],"category_scores_gemma":[0.01963666,0.0002619585,0.0009401454,0.003716015,0.0008159928,0.001915932,0.001635956,0.0003956046,0.0006303395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009756177,"about_ca_system_score_gemma":0.0008615846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001510864,"about_ca_topic_score_gemma":0.001820911,"domain_scores_codex":[0.9931809,0.002379296,0.0005141699,0.0003806358,0.003265564,0.0002793537],"domain_scores_gemma":[0.9855447,0.007365829,0.001175616,0.0006778283,0.004708081,0.0005278893],"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":[0.006294342,0.00188337,0.04643129,0.005445589,0.0006255468,0.000732469,0.002079856,0.00580754,0.008676645,0.004171026,0.00404756,0.9138048],"study_design_scores_gemma":[0.001677567,0.04872999,0.4973237,0.01177071,0.005797147,0.01383654,0.02740854,0.2075524,0.06792879,0.01113327,0.1058942,0.0009470972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8717963,0.04237689,0.05448283,0.0006349278,0.0005518956,0.003966006,0.002178152,0.001089886,0.02292315],"genre_scores_gemma":[0.9147549,0.01803559,0.06189682,0.000111529,0.0001597137,0.0009903585,0.001266708,0.00008505985,0.002699233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01268918,"threshold_uncertainty_score":0.03917825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02375739215731539,"score_gpt":0.2688546815892903,"score_spread":0.245097289431975,"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."}}