Bibliographic record
Abstract
This chapter presents a comparison of activity between cross rates and USD major currencies for the time frame 1/1/2005 through 4/14/2006. By arranging the five major currencies, EUR, GBP, USD, CHF, JPY, in a two-dimensional matrix format and adding each row, the total activity of each individual currency can be determined. The chapter presents a table of two-dimensional matrix format and values are expressed in millions of ticks there. It also presents a table of currencies sorted by activity percentage. By virtue of the fact that the Economic and Monetary Union (EMU) is composed of twelve member nations and three of these are members of the Group of Eight (G8), the chapter anticipates a much higher position for the Euro. G8, consisting of the United States, UK, Japan, Germany, France, Italy, Canada, and the Russian Federation, represents 67% of the world economy. Therefore, for the table of currencies sorted by activity percentage presented in the chapter, the same analysis was performed on the same five currencies but with an earlier time frame spanning 1/1/2000 through 12/31/2003. This confirms the rather obvious hypothesis that the currency markets are in perpetual flux and that indicators that may be valid at one time may not be valid at a later time. Also it is estimated that these five currencies comprise 78% of all trading in the foreign exchange markets.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.054 |
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".