THE EFFECTS OF URANIUM PRICE FLUCTUATIONS ON PRODUCTION, EXPLORATION EXPENDITURES AND RESERVES: VAR APPROACH
Bibliographic record
Abstract
The aim of this paper is to empirically analyse the effects of uranium price fluctuations, i.e. increase vs decrease, on uranium production, uranium exploration expenditures and uranium reserves. We apply a Vector Autoregression (VAR) approach which allows for both symmetric and asymmetric model specifications to simulate impulse-response functions (IRFs) and derive the forecasting error variance decomposition (VD). Results give evidence that a uranium price increase induces an exploration expenditures increase and, to a lesser extent, a production increase. In contrast, no significant effect of uranium price fluctuations on uranium reserves can be supported. Results also give evidence of the presence of asymmetric aspects in the response of uranium exploration expenditures and uranium production to uranium price fluctuations. In fact, uranium exploration expenditures and uranium production seem to be more sensitive to uranium price increases than to uranium price decreases.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".