Bibliographic record
Abstract
This paper, according to the Ayatollah Khamenei's statement, removes US dollor in economic transactions.And also in order to achieve Resistive economy, since most of our economic transactions are oil transactions, this paper need to choose a vehicle currency other than the dollar and enter into our transactions.As shown in this paper, can be seen that most of Iran's oil transactions are done with China; for the reasons that will be discussed later, between the currency of Iran and China, Yuan is the best currency to choose as a vehicle currency in oil trades.This paper concluded that: the pass-through effects of the yuan's appreciations on prices of China imports and that of Japan differ substantially.While pass-through effects on China import prices are relatively weak, about 23% in the short run and less than 47% in the long run, I did not find any evidence that the yuan's appreciation from July 2007 to 2010 was passed into prices of Japanese import from the IRI.So yuan is the best Selection vehicle currency for Iran's oil contracts.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.951 | 0.953 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".