Bibliographic record
Abstract
This paper discusses the present and predicted global need for uranium, and the geophysical exploration challenges for the location, mapping, and evaluation of the new deposits needed to address the projected supply shortfall. In 2008, approximately 60% of the world’s uranium production was from Canada, Australia and Kazakhstan. Africa contributed 18.5 %, derived, in order of supply, from Namibia, Niger and South Africa (WNA, 2009). The major requirement for uranium stems from the needs of both developed and rapidly developing countries who do not necessarily have uranium resources of their own but who have expansive nuclear power generation plans, in particular China and India. The potential for Africa to become a leading supplier is immense. The major challenge thus posed to the geosciences is for the refining of resources to proven reserves, and for the location and evaluation of new deposits. In this respect geophysics has played, and continues to play, a leading role in every aspect of the nuclear fuel cycle, including: the direct exploration for uranium, mapping under cover to ever increasing depths, borehole logging for ore reserve evaluation, ore sorting on mines, environmental monitoring, and nuclear waste disposal. In this presentation, some of the main geophysical advances in uranium exploration technology, and the geophysical challenges to uranium exploration, are addressed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".