{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001293627,0.0002764073,0.000226781,0.0002047694,0.0001252886,0.002742637,0.001001229,0.0001615388,0.000001762497],"category_scores_gemma":[0.00008259607,0.0002462037,0.0002634539,0.0002498105,0.00003913284,0.0005807914,0.0007859733,0.001647608,0.0001264536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001225012,"about_ca_system_score_gemma":0.00243307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003573345,"about_ca_topic_score_gemma":0.00002747477,"domain_scores_codex":[0.997096,0.00002614045,0.0005074303,0.0005456935,0.000465846,0.001358915],"domain_scores_gemma":[0.9987494,0.00004731054,0.0002962493,0.0004739913,0.0003669395,0.00006608998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000005365462,0.00002101445,0.000003855035,0.00003226195,0.0001036712,0.000001857395,0.00009852598,0.00006892506,0.00001399576,0.5500231,0.0004217734,0.4492056],"study_design_scores_gemma":[0.0001618312,0.0002127833,0.00007532529,0.00007738477,0.0000464788,0.0001675363,0.00007354996,0.004135166,0.0002105175,0.9922027,0.002346525,0.0002902187],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1315671,0.004608887,0.8488717,0.00213842,0.007715156,0.0009705338,0.0000410959,0.0003610972,0.003726014],"genre_scores_gemma":[0.9885506,0.0003895762,0.0008146811,0.00004475159,0.000562679,0.00003982076,0.00009163065,0.00003536444,0.009470916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8569835,"threshold_uncertainty_score":0.999999,"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."}}