Análisis de las exportaciones de oro del Perú: Comportamiento a corto y largo plazo (2009-2019)
Bibliographic record
Abstract
Gold is one of the products that contributes the most to the value of Peru’s mining exports. In the period 2009-2019, the value of Peru’s gold exports showed a fluctuating behavior in the short term and a growing trend in the long term. The work shows the influence of the price of gold on fluctuations in the value of exports and the effect of the volume exported on the trend in the value of Peru’s gold exports. A direct long-term relationship is observed between the behavior of the price of gold and the stock indices of China, Switzerland and India, countries that are among the main importers of gold worldwide, which allows us to infer that the behavior of the economies of the aforementioned countries has influenced the price of gold. The increase in the number of the main destination countries for Peruvian gold, going from three (2009) to five countries (2019), has made it possible to maintain a growing trend in the value of Peru’s gold exports. The total demand for gold in these five countries (Switzerland, Canada, the USA, India and the United Arab Emirates) would have been influenced by the behavior of their respective GDPs, an influence that would have been conditioned by the situation of the world economy. Descriptive-comparative analysis, linear regression and correlation have been applied to the time series to identify the relationships between variables
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".