Survival Analysis of Bank Note Circulation: Fitness, Network Structure and Machine Learning
Bibliographic record
Abstract
"The main objective of this research is to understand bank note distribution patterns. We do this in a novel way—by looking at bank notes through the lens of big data analytics. We use data from the Bank of Canada's Currency Information Management Strategy (IMS) to study the notes themselves. We perform an exploratory analysis of the networks spanned by the bank notes’ circulation across 10 regions in Canada. The analysis focuses on how long the bank notes stay in the market, i.e., their duration in circulation. We also develop an empirical framework to establish what determines a bank note’s duration in the market. Our results show that the physical fitness of a bank note determines its duration in circulation. We also find that the denomination of the bank note is even more relevant to the time it spends in the market. This suggests that the denomination of a bank note may be related to the type of transaction, and that these transactions may correspond to the bank note’s likelihood of being deposited back to the Bank of Canada. This finding is informative because it directly speaks to the determinants of the distribution patterns and serves as a starting point for interesting questions: What denominations are relevant for particular transactions, and why do these transactions dramatically change a bank note’s duration in circulation?"
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".