From LVTS to Lynx: Quantitative assessment of payment system transition in Canada
Bibliographic record
Abstract
Modernising Canada’s wholesale payments system from the legacy large-value transfer system (LVTS) to Lynx brings two key changes: (1) the settlement model shifts from a hybrid system that combined components of both real-time gross settlement (RTGS) and deferred net settlement to an RTGS system; and (2) the policy regarding queue usage changes from discouraging it to encouraging the adoption of the new liquidity-saving mechanism. This paper quantitatively assesses the effects of these changes on the behaviour of participants in the payments system. The analysis reveals that most system-level payments in Lynx are settled in a single stream via the liquidity-saving mechanism, thus facilitating liquidity pooling and leading to higher efficiency than with LVTS, where payments were distributed in two streams. Moreover, due to Lynx’s liquidity-saving mechanism, many payments arrive earlier than those settled in LVTS, providing more opportunities for liquidity saving at the cost of slightly increased payment delay. At the participant level, meanwhile, the analysis suggests that liquidity efficiency is improved for various participants, with most experiencing slightly longer payment delays in Lynx than in LVTS.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".