{"id":"W4413179682","doi":"10.1109/netcrypt65877.2025.11102700","title":"Crime Scene Object Detection for Forensic Investigations Using Faster R-CNN and YOLOv5 Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Crime scene; Artificial intelligence; Object detection; Forensic science; Object (grammar); Computer vision; Pattern recognition (psychology); Criminology; Archaeology; History; Psychology","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":[0.0007736319,0.001192034,0.0006568469,0.00105247,0.0002467421,0.0009985613,0.001727437,0.0009777509,0.002533498],"category_scores_gemma":[0.001654456,0.0004324105,0.001039751,0.0004902759,0.0002722024,0.001182401,0.0007560829,0.0009332809,0.001256629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001248457,"about_ca_system_score_gemma":0.001077779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01438902,"about_ca_topic_score_gemma":0.01665531,"domain_scores_codex":[0.9996753,0.00004079273,0.00001883279,0.0001053795,0.00009323179,0.00006651097],"domain_scores_gemma":[0.9995591,0.0001030447,0.00007189768,0.00006774741,0.0001725582,0.00002567229],"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":[0.0005506542,0.0002670816,0.008013695,0.0002743912,0.0002670037,0.0003034874,0.0001005545,0.3843694,0.0244899,0.004352832,0.008586361,0.5684247],"study_design_scores_gemma":[0.000006380523,0.00007988782,0.0009525447,0.00002383899,0.00003749219,0.00007712121,0.00001561638,0.9914346,0.005378248,0.0006369586,0.001347289,0.00001010433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2059911,0.003426515,0.7697048,0.0009839729,0.0004693724,0.0002430337,0.001108094,0.00814382,0.009929297],"genre_scores_gemma":[0.7598436,0.001496448,0.2242672,0.0005238394,0.00009430952,0.000142015,0.002308628,0.0002015484,0.01112248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01438902,"threshold_uncertainty_score":0.02861053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03591028596697537,"score_gpt":0.2613236217379689,"score_spread":0.2254133357709935,"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."}}