{"id":"W4383999138","doi":"10.1111/1556-4029.15319","title":"Automated detection of regions of interest in cartridge case images using deep learning","year":2023,"lang":"en","type":"article","venue":"Journal of Forensic Sciences","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Ultra Electronics (Canada)","funders":"","keywords":"Cartridge; Artificial intelligence; Computer science; Computer vision; Engineering; Mechanical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0006837332,0.00006462912,0.0001909731,0.00009929171,0.0001247697,0.00002154472,0.0001365586,0.00004304127,0.000007761537],"category_scores_gemma":[0.0002054562,0.00002234763,0.0000996857,0.001644797,0.0002005118,0.000216587,0.00003465136,0.0001140883,8.537183e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001542984,"about_ca_system_score_gemma":0.00001292366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004365807,"about_ca_topic_score_gemma":0.002091432,"domain_scores_codex":[0.999155,0.00007283228,0.0003700337,0.00009201122,0.0001614188,0.0001487185],"domain_scores_gemma":[0.999159,0.0002138509,0.0004133474,0.00001746053,0.0001559533,0.00004043746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001370156,0.00002888353,0.01818069,0.000007610068,0.000008756156,0.0001757313,0.0003265011,0.0009888051,0.9562656,0.00003978262,0.0001512749,0.02381269],"study_design_scores_gemma":[0.0004313276,0.002166915,0.5159538,0.0004785241,0.0000600244,0.004375152,0.02099364,0.01509169,0.4387359,0.001119837,0.0003220115,0.0002711999],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993384,0.0001421917,0.000007554095,0.000205018,0.0001989685,0.00003807184,0.000001806919,0.00001987492,0.00004807722],"genre_scores_gemma":[0.9997004,0.00003767226,0.0001469042,0.000007015681,0.0000970678,2.80481e-7,6.325973e-7,3.433675e-7,0.000009716178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5175296,"threshold_uncertainty_score":0.1167068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08923597042281778,"score_gpt":0.2876629554695063,"score_spread":0.1984269850466885,"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."}}