{"id":"W2133528180","doi":"10.1117/12.524865","title":"Perception and detection of counterfeit currency in Canada: note quality, training, and security features","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Counterfeit; Computer science; Cash; Currency; Quality (philosophy); Feature (linguistics); Computer security; Perception; Artificial intelligence; Test (biology); Business; Finance; Psychology; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006868381,0.0003185439,0.0002652614,0.0005620436,0.001153014,0.001004244,0.0005132033,0.0004318204,0.00259699],"category_scores_gemma":[0.00604745,0.0001981135,0.000162867,0.0006337871,0.0007712532,0.0003523044,0.0006324174,0.0004212137,0.0002098689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007346939,"about_ca_system_score_gemma":0.00535405,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9570753,"about_ca_topic_score_gemma":0.9723207,"domain_scores_codex":[0.9993649,0.00006391275,0.00002220369,0.0000763309,0.000346777,0.0001259343],"domain_scores_gemma":[0.9974347,0.0004171632,0.0003745286,0.00009971891,0.001273442,0.0004004964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002360488,0.000268538,0.8545912,0.0001731662,0.00009289131,0.0006262385,0.01725912,0.001200311,0.03131023,0.0004002482,0.00326451,0.08845313],"study_design_scores_gemma":[0.00001687564,0.0001529316,0.9885194,0.00002252579,0.00001842103,0.0001385093,0.005861429,0.001113312,0.002418161,0.00004966028,0.001657249,0.00003161474],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980878,0.00008793069,0.0001342601,0.00007181506,0.000004065024,0.00001194841,0.0001115045,0.000008005413,0.001482573],"genre_scores_gemma":[0.9975258,0.0001126452,0.0003982717,0.00003307224,0.000001797019,0.000004823725,0.000121807,0.000003963705,0.001797912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0429247,"threshold_uncertainty_score":0.08635491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557602026604568,"score_gpt":0.247455271686612,"score_spread":0.2318792514205663,"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."}}