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Record W2348420528

An Analysis and Forecast of the RMB Real Effective Exchange Rate Based on Neural Network

2005· article· en· W2348420528 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe International Trade Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicGeoscience and Mining Technology
Canadian institutionsnot available
Fundersnot available
KeywordsRenminbiMargin (machine learning)Quarter (Canadian coin)Exchange rateGovernment (linguistics)Index (typography)EconomicsEffective exchange rateExpectancy theoryForeign exchangeMonetary economicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper Calculates the RMB real effective exchange rate index from first quarter of 1994 to the second quarter of 2004 and forecasts its tendency utilizing the self-adaptation neural network technology. The result shows that since 1994, the RMB real effective exchange rate index has been on the steady increase and will keep rising to a little extent in the near future. So, the actual foundation on which the international community requires RMB to appreciate by a wide margin does not exist, and the essence ofRMB should appreciateview is the appreciation expectancy of foreign government and media caused by the depression of international economy. So, the government should take the effective measure actively to evacuate the RMB appreciation pressure and reduce its danger to minimum extent.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.235
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it