Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the foreign exchange market, where average daily turnover is in trillions of dollars and trades span time zones, legal systems, and domestic payments systems, participants take on various risks. The most serious risk is credit risk - the risk that one party will fail to pay. Central banks, private sector financial institutions, and domestic payments systems operators laboured for more than a decade to develop a multi-currency settlement system to deal with these risks. The result, the CLS Bank, began operations in September 2002. It virtually eliminates the credit risk inherent in foreign exchange transactions by providing a payment-versus-payment arrangement for settlement. The CLS Bank is regulated by the Federal Reserve Board in consultation with the central banks that have currencies settling through its system. At present there are seven currencies, including the Canadian dollar. The Bank of Canada acts as banker for the CLS Bank, providing it with a settlement account and making and receiving payments on its behalf through the Large Value Transfer System. With the participation and support of the world's largest foreign-exchange-dealing institutions, and growing membership, the CLS Bank has the potential to become the dominant global mechanism for settling foreign exchange transactions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it