Bibliographic record
Abstract
A Payment system is a mechanism whereby fund settlements can be smoothly effected and, as such, is an important public infrastructure supporting economic activity and financial transactions. Payment-related operations are one of the core businesses of banking industry. Meanwhile for central banks, it is one of the key roles to ensure secure and efficient payment systems. Looking at payment systems worldwide, three new trends are apparent. The first is the introduction of RTGS（Real-time Gross Settlement）system into the payment systems operated by central banks. The second is adoption of“hybrid systems”by private payment systems. A hybrid system is a net settlement system that netting and settlement are carried out frequently or continuously during the daytime. The third trend is to build an “integrated system”, a payment system which has both RTGS and net settlement functions. The first integrated system was LVTS in Canada and the Deutsche Bundesbank recently introduced an integrated system called “RTGSplus”. Japanese payment systems should keep up with these new trends and try to conform to global standards. Possible reform in this context would be to make the Foreign Exchange Yen Clearing System a hybrid system. Another possibility would be to change the allocation of roles between the Foreign Exchange Yen Clearing System and the Zengin system.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".