{"id":"W4229450351","doi":"10.1080/00450618.2022.2074138","title":"The potential of using the forensic profiles of Australian fraudulent identity documents to assist intelligence-led policing","year":2022,"lang":"en","type":"article","venue":"Australian Journal of Forensic Sciences","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Profiling (computer programming); Crime scene; Identity (music); Offender profiling; Terrorism; Computer science; Identity theft; Computer security; Crime analysis; Intelligence analysis; Data science; World Wide Web; Criminology; Political science; Artificial intelligence; Psychology; Law","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":[0.002199116,0.0001438129,0.0002356231,0.0001375305,0.0006482186,0.00008253093,0.00117949,0.0000446941,0.00006181378],"category_scores_gemma":[0.0001759444,0.00008497951,0.0002372926,0.000587626,0.001434395,0.00002527764,0.0003656395,0.0002154608,8.151532e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003631246,"about_ca_system_score_gemma":0.0003413855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004146066,"about_ca_topic_score_gemma":0.0001254908,"domain_scores_codex":[0.9973186,0.0002239529,0.000704967,0.00021615,0.0011238,0.0004125063],"domain_scores_gemma":[0.9985692,0.00004430052,0.0006098177,0.0003053361,0.0003483754,0.0001230001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009240285,0.0002683598,0.06416226,0.0000950985,0.0007394559,0.00004424532,0.00177564,0.08833139,0.7641149,0.003867888,0.02827272,0.04740404],"study_design_scores_gemma":[0.0004759368,0.003131145,0.03331758,0.0001171468,0.0001213409,0.0004084416,0.01831002,0.0002437215,0.9352397,0.006034387,0.002322131,0.0002784125],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957424,0.0001546567,0.0008105033,0.002274296,0.000696664,0.0002302748,0.00001706855,0.000001159429,0.0000729329],"genre_scores_gemma":[0.9968031,0.00004248068,0.002336118,0.00005713648,0.0001723071,0.000003919653,0.000002062796,0.000007924789,0.0005749242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1711249,"threshold_uncertainty_score":0.5285087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04333179910449278,"score_gpt":0.3627671903263857,"score_spread":0.3194353912218929,"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."}}