Adaptive Neuro-Fuzzy Optimization of the Net Present Value and Internal Rate of Return of a Wind Farm Project under Wake Effect
Bibliographic record
Abstract
According to studies on the impossible trinity, under conditions of high financial integration, the domestic interest rate is closely linked to the foreign one if the possibility of maneuvering interest rates is absent in this transaction. The Fisher effect is brought into this escapade because interest rates generally trend positively with inflation. Botswana has set her inflation target between 3-6% and this study attempts to determine inflation spillover effects from the United Kingdom, United States of America, Canada, Japan, China, Belgium, France, Germany, South Africa, Nigeria, and Ghana using data from 1980-2012. Comparatively, the attempts made by previous studies to examine spillovers generally lacked a long-run focus and channeled much attention to periods of financial crisis. This study deviates from other studies by using the Augmented Dickey Fuller (ADF) test to examine unit roots for the countries under examination. The study further applies the Johansen cointegration procedure, as well as the Granger causality test. The results show that Botswana’s inflation dynamics trend positively with all the countries under scrutiny except South Africa in a long-run framework. However, the Granger causality test only proved that Botswana’s inflation lead China’s inflation dynamics. In conclusion, Botswana’s inflation is not driven by other countries’ inflation dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".