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Record W2802033989 · doi:10.1080/1478422x.2018.1463009

Corrosion of copper-coated used nuclear fuel containers due to oxygen trapped in a Canadian deep geological repository

2018· article· en· W2802033989 on OpenAlexafffundabout
David S. Hall, Thalia E. Standish, Mehran Behazin, Peter Keech

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsWestern UniversityNuclear Waste Management OrganizationDalhousie University
FundersMitacsNuclear Waste Management Organization
KeywordsSpent nuclear fuelCorrosionRadioactive wasteCopperAllowance (engineering)Environmental scienceClosure (psychology)Materials scienceMetallurgyNuclear fuelWaste managementNuclear engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The Nuclear Waste Management Organization is evaluating the safety and feasibility of the permanent disposal of used nuclear fuel in a deep geological repository. Their current design concept utilises copper-coated steel used fuel containers to isolate the waste from the environment. Immediately following repository closure, a finite quantity of O2 will be trapped inside the repository and could cause some amount of oxic corrosion to the outer copper layer of the containers. On a per container basis, 13 mol of O2 will be trapped in the repository rooms at the time of closure, based on reference design dimensions. This corresponds to a maximum depth of copper corrosion of 81 μm, assuming a uniform distribution. This work also considers the sensitivity of this oxic corrosion allowance to various hypothetical design changes to the repository that may occur before or during construction.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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 designBench or experimental
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

Citations25
Published2018
Admission routes3
Has abstractyes

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