{"id":"W4412930278","doi":"10.1016/j.fsidi.2025.301972","title":"Tool type identification for forensic digital document examination","year":2025,"lang":"en","type":"article","venue":"Forensic Science International Digital Investigation","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"University of Winnipeg","keywords":"Identification (biology); Forensic science; Digital forensics; Forensic identification; Forensic examination; Computer science; Type (biology); Digital evidence; Data science; Computer security; Engineering; Forensic engineering; Geography; Archaeology; Biology; Paleontology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001679333,0.0007860613,0.0005725042,0.003114675,0.0006557132,0.0015474,0.001413166,0.001420191,0.003809894],"category_scores_gemma":[0.008184874,0.0003332181,0.0008060456,0.001186406,0.000716305,0.003396966,0.00143263,0.001560781,0.00247841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001135014,"about_ca_system_score_gemma":0.00118972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002543278,"about_ca_topic_score_gemma":0.003543628,"domain_scores_codex":[0.9989158,0.0001805753,0.00006909882,0.0002182848,0.0004985621,0.0001176566],"domain_scores_gemma":[0.9962459,0.001130762,0.0005035325,0.0008655347,0.00112562,0.00012855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004380104,0.0002768827,0.01805229,0.0003661959,0.00007382949,0.0005061967,0.0003430087,0.04464192,0.05026861,0.01043056,0.007877275,0.8667253],"study_design_scores_gemma":[0.00002879833,0.0001932001,0.009698579,0.0002820909,0.00008799753,0.001420307,0.0004875705,0.8215629,0.1267419,0.01770944,0.02170231,0.0000849547],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1195433,0.001181689,0.8613931,0.0008616296,0.0002313212,0.0004052358,0.001080245,0.007450982,0.007852498],"genre_scores_gemma":[0.5680135,0.0007144543,0.4250053,0.0002410736,0.00005651081,0.000166159,0.001458551,0.0002768378,0.004067528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003809894,"threshold_uncertainty_score":0.01274538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01647634175532159,"score_gpt":0.2632042401191722,"score_spread":0.2467278983638506,"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."}}