Visual and optical evaluation of bank notes in circulation
Bibliographic record
Abstract
A method for comparing quality of bank notes in circulation based on both a subjective visual sorting technique and on quantitative wear evaluations is described and applied to circulated Canadian bank notes. The sample notes, which were part of a $5 circulation trial, issued over a 4 to 6 week period, had been in circulation for roughly 6 months. Notes were first sorted visually into four defined substrate categories (No Edge Wear, Corner Folds, Minimal Edge Wear and Edge Wear) and four surface wear categories (None, Low, Medium and High). Samples of each category were tested at Crane and Co. using a range of physical and optical techniques: air resistance, air permeability, stiffness deflection, double folds, gray scale, brightness, perimeter length, and top/bottom mean and maximum deviations. The visual sort showed that neither soiling nor ink loss are the major wear problems for bank notes in Canada. However, the substrate does become tattered and worn. The mechanical and optical wear tests show that most of the parameters change logically as the soil level increases. The changes for other parameters are less clear as a function of wear categories, but are relatively consistent in distinguishing between the No Edge Wear and Edge Wear. Impact of wear on the effectiveness of security features will also be described.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".