Simulating Intraday Transactions in the Canadian Retail Batch System
Bibliographic record
Abstract
This paper proposes a unique approach to simulate intraday transactions in the Canadian retail payments batch system. Such transactions are currently unobtainable. The simulation procedure, though demonstrated in the realm of payments systems, has tremendous potential for helping with data-deficient problems where only high-level aggregate information is available. The approach uses the concept of integer composition in combinatorics to break down the daily total value and volume (available) into individual data points (unavailable) throughout the day. The algorithm also introduces a technique to incorporate any intraday timing pattern (known or hypothetical) to make simulated data closer to reality. Simulation results show that the probability distribution of individual payment values is remarkably stable through repeated random sampling. This suggests a high degree of accuracy and viability of this simulation method. In addition, the densities of simulated intraday transactions are found to be invariably skewed to the left of the mean payment value, which reflects the nature of retail payments systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".