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Record W2072085799 · doi:10.1115/icone21-16511

Centralization of Canada’s Spent Nuclear Fuel

2013· article· en· W2072085799 on OpenAlexaffabout
Krista Nicholson, John McDonald, Shona Draper, B.M. Ikeda, Igor Pioro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSpent nuclear fuelContainment (computer programming)Radioactive wasteNuclear powerInterimSite selectionWaste managementContainer (type theory)High-level wasteNuclear fuelEnvironmental scienceEngineeringTransport engineeringNuclear engineeringComputer science

Abstract

fetched live from OpenAlex

Currently in Canada, spent fuel produced from Nuclear Power Plants (NPPs) is in the interim storage all across the country. It is Canada’s long-term strategy to have a national geologic repository for the disposal of spent nuclear fuel for CANada Deuterium Uranium (CANDU) reactors. The initial problem is to identify a means to centralize Canada’s spent nuclear fuel. The objective of this paper is to present a solution for the transportation issues that surround centralizing the waste. This paper reviews three major components of managing and the transporting of high-level nuclear waste: 1) site selection, 2) containment and 3) the proposed transportation method. The site has been selected based upon several factors including proximity to railways and highways. These factors play an important role in the site-selection process since the location must be accessible and ideally to be far from communities. For the containment of the spent fuel during transportation, a copper-shell container with a steel structural infrastructure was selected based on good thermal, structural, and corrosion resistance properties has been designed. Rail has been selected as the method of transporting the container due to both the potential to accommodate several containers at once and the extensive railway system in Canada.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.898

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)0.0010.000

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 head0.004
GPT teacher head0.149
Teacher spread0.145 · 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 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

Citations0
Published2013
Admission routes2
Has abstractyes

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