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Record W2460202503

Recent activities and trends in the uranium market

2006· article· en· W2460202503 on OpenAlexaboutno aff
R. Kwasny, F. Aul, K. Lohrey

Bibliographic record

VenueAtw. Internationale Zeitschrift fuer Kernenergie · 2006
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)UraniumMillBusinessEconomyGeographyEconomicsArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Concerns about the impact of hydrocarbon use on climate and global warming are significantly growing. Furthermore, we are all well aware that security of supply is increasingly an issue. In this context, it is now principally recognised that nuclear energy has to be back on the agenda. All in all, the prospects for the nuclear power industry and thus for the uranium activities is very positive for the coming years. The changes that have taken place in the international uranium market during the past several years are remarkable. Since 2002, the uranium prices have increased more than tenfold. The spot market price of uranium began an increase from about USD 9/lb U 3 O 8 in mid 2001 following a fire at the Olympic Dam mill (Australia) in October 2001 and was propelled in subsequent years by a series of interrupting events, such as the mine shaft flooding at the McArthur River mine (Canada) in April 2003, the threat of the early shutdown of the Rossing mine (Namibia) and the Ranger mine (Australia) in 2003, the decision of Techsnabexport (Tenex, Russia) in October 2003 to terminate sales of UF6 to the US trading company Globe Nuclear Services and Supply GNSS Ltd. (GNSS), and finally the complete flooding at the developing Cigar Lake mine (Canada) in October 2006. With the emergence of hedge funds and investors, that began in late 2004, increased uranium demand and upward pressure on market prices were further stimulated. What about the recent events and trends in the uranium industry? Are the uranium producers and the utilities well prepared to meet all the challenges associated with developments in the uranium business? And what about the risks, uncertainties and other factors that could affect the developments in the uranium industry and uranium markets?

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0040.000

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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.

Study designNot applicable
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

Citations3
Published2006
Admission routes1
Has abstractyes

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