Government Bonds, Yields, Yield Curves, and Currency Prices
Bibliographic record
Abstract
This chapter contains sections titled: Yield Curves Currency Trading and Yield Curves Central Banks and Yield Curves Bonds and Yields Euro/U.S. Dollar and U.S. Treasury Bond Yields British Pound/U.S. Dollar and Bond Yields U.S. Dollar/Swiss Franc, U.S. Dollar/Canadian Dollar, U.S. Dollar/Japanese Yen and Bond Yields Carry Trades and Bond Yields U.S. Treasury Yield Curves and 2- and 10-Year Notes U.S. Dollar/Swiss Franc, U.S. Dollar/Japanese Yen, and U.S. Dollar/Canadian Dollar Canada Yield Curve and Bond Issuance Calculate Canada Bonds and Yields Yield Curve and U.S. Dollar/Canadian Dollar Australian Dollar/U.S. Dollar and New Zealand Dollar/U.S. Dollar British Pound Yield Curve Gilt Issuance British Pound/U.S. Dollar Japanese Yield Curves Japanese Yield Curve and U.S. Dollar/Japanese Yen U.S. Dollar/Japanese Yen, Bonds, and Yields Australia Yield Curve Factor Australia Yield Curve Australian Dollar/U.S. Dollar and Australia Yield Curves Track Australian Dollar/U.S. Dollar New Zealand Inflation-Indexed Bonds Factored as a Settlement Price per New Zealand Dollar as Principal New Zealand Dollar/U.S. Dollar and New Zealand Yield Curves Track New Zealand Dollar/U.S. Dollar Australian Dollar/New Zealand Dollar and Yield Curves Euro Yield Curve Track the Euro Yield Curve Euro/British Pound and Yield Curve Swiss Franc Yield Curve Swiss Yield Curve U.S. Dollar/Swiss Franc U.S. Yield Curve Dollar Pairs and Yield Curves Reserve Requirements and Bonds Cross Pairs, Bonds, and Yields Trade Strategies Yield Curves and Currency Prices Dollar Value of Basis Point and Modified Duration Conclusion
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.021 |
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".