{"id":"W7103651999","doi":"10.5281/zenodo.17500003","title":"Admissibility of AI-Generated Forensic Evidence: Legal Standards, Ethical Challenges, and Comparative Jurisprudential Analysis","year":2025,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transparency (behavior); Digital forensics; Identification (biology); Process (computing); Digital evidence; Black box; Criminal investigation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.1437824,0.000658021,0.00179037,0.009468886,0.01247711,0.03048662,0.006521684,0.02302722,0.002182544],"category_scores_gemma":[0.1519647,0.001026326,0.001069141,0.004373148,0.1099012,0.02169458,0.0109383,0.01538382,0.0005845677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01367672,"about_ca_system_score_gemma":0.0142774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004214977,"about_ca_topic_score_gemma":0.003091204,"domain_scores_codex":[0.8865521,0.06921344,0.007578566,0.005679341,0.0283424,0.002634191],"domain_scores_gemma":[0.7926452,0.1730516,0.007142551,0.01265163,0.01303233,0.001476634],"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":[0.00000237965,0.000008418252,0.00005800396,0.00003553256,0.000003535943,0.00003644266,0.001180831,0.0001638374,0.0000344369,0.9945768,0.0006052287,0.003294562],"study_design_scores_gemma":[0.000006021961,0.00001339848,0.0001553351,0.0004310767,0.000006864309,0.00009304081,0.001179804,0.0005417652,0.0002033988,0.9803817,0.01696795,0.00001964117],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0351027,0.06950338,0.1518773,0.321876,0.001847195,0.0002561084,0.0001693146,0.0001606393,0.4192074],"genre_scores_gemma":[0.8982107,0.0155012,0.05618441,0.02080618,0.002163312,0.0005865352,0.0001381781,0.0001312522,0.006278206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1437824,"threshold_uncertainty_score":0.7604032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065462630096976,"score_gpt":0.3366105743316434,"score_spread":0.2300643113219458,"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."}}