Perception and detection of counterfeit currency in Canada: note quality, training, and security features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Commissioned by the Bank of Canada to help improve the detection of counterfeit currency, we designed a series of tests of performance to explore the contributions of note quality, sensory modality, training, security features and demographic variables to the accuracy of counterfeit detection with three different note types. In each test, participants (general public, and cash handlers, divided amongst commercial cash handlers and bank tellers) were presented with notes, one at a time, for up to seven seconds, and were asked to judge whether each note was genuine or counterfeit. With whole note inspection, overall accuracy was about 80%. When the security features were tested individually, the Optical Security Device (OSD) was the best feature, the hidden number was the worst, and the portrait, maple leaves, fluorescence, and microprinting were intermediate. Accuracy was higher with notes that could be seen but not touched than vice versa. Cash handlers were 74% correct with touch alone and adding touch to vision significantly improved counterfeit detection. This paper will demonstrate how performance differences between the different note types can be explained in terms of the efficacy of the individual security features incorporated into the notes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it