{"id":"W4403205302","doi":"10.2139/ssrn.4980749","title":"Tool Type Identification for Forensic Digital Document Examination","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Identification (biology); Digital forensics; Forensic science; Forensic examination; Forensic identification; Computer science; Computer forensics; Type (biology); Digital evidence; Data science; Computer security; Engineering; Geography; Forensic engineering; Archaeology; Geology; Biology; Paleontology","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":[0.002629154,0.001270391,0.001240236,0.008008296,0.002150715,0.005569006,0.002131555,0.003515609,0.02368974],"category_scores_gemma":[0.01940096,0.0008498746,0.0008567444,0.003693239,0.0009267856,0.005498878,0.003557424,0.001917727,0.02212523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007548594,"about_ca_system_score_gemma":0.002509382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001205984,"about_ca_topic_score_gemma":0.001403614,"domain_scores_codex":[0.9948086,0.0007208784,0.000623,0.0007375624,0.002557396,0.0005525691],"domain_scores_gemma":[0.9839409,0.003410738,0.00143876,0.005889771,0.004778086,0.0005417039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001171625,0.0003104723,0.01309678,0.0008578954,0.00005493562,0.001659756,0.0006666234,0.002966278,0.05259578,0.03936645,0.03985874,0.8473946],"study_design_scores_gemma":[0.0001532724,0.0007210222,0.01569583,0.002190686,0.0003627774,0.01369674,0.001930882,0.1777209,0.3263351,0.1197922,0.3409788,0.0004216506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0270461,0.00111233,0.927439,0.0005378624,0.000573535,0.0005934768,0.003172742,0.01904277,0.02048221],"genre_scores_gemma":[0.2606437,0.0009665373,0.7065242,0.00039207,0.000202936,0.0003629748,0.005279052,0.002217003,0.02341148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02368974,"threshold_uncertainty_score":0.0792501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009330862951353358,"score_gpt":0.241748477543684,"score_spread":0.2324176145923306,"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."}}