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The Uranium Boom: a Challenge to Geophysical Exploration

2009· article· en· W3020946761 on OpenAlexaboutno aff
B. Corner

Bibliographic record

Venue11th SAGA Biennial Technical Meeting and Exhibition · 2009
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsUraniumExpansiveMining engineeringUranium oreNuclear powerGeologyBoomEarth scienceEnvironmental scienceOceanography

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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