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Record W4378191085 · doi:10.1002/maco.202313762

Reactive‐transport model for the production, transport, and consumption of sulfide in a spent nuclear fuel deep geological repository in crystalline rock

2023· article· en· W4378191085 on OpenAlexaff
Chi‐Che Hung, Scott Briggs, Yun‐Chen Yu, Yuan‐Chieh Wu, Fraser King

Bibliographic record

VenueMaterials and Corrosion · 2023
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsNuclear Waste Management Organization
Fundersnot available
KeywordsSulfideSulfateCorrosionMackinawiteDissolutionFlux (metallurgy)Spent nuclear fuelEnvironmental scienceChemistryGeologyMetallurgyMaterials scienceNuclear chemistry

Abstract

fetched live from OpenAlex

Abstract A 1‐D reactive‐transport model has been developed to describe the production and transport of sulfide in a deep geological repository in crystalline rock and the subsequent corrosion of the copper canister. The model accounts for various processes, including: (i) the microbial reduction of sulfate by organotrophic and lithotrophic sulfate‐reducing bacteria, (ii) the supply of sulfate from both the ground water and from the dissolution of gypsum present as an accessory mineral in the bentonite buffer, (iii) diffusive transport of reactants and products, and (iv) sequestration of a fraction of the microbially produced sulfide by precipitation as mackinawite. The results of a base case simulation and of sensitivity analyses indicate that the extent of uniform corrosion is approximately 0.2 mm after 1 million years and that the maximum flux of sulfide to the canister surface is below the threshold for localized corrosion or stress corrosion cracking.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.301

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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