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Record W4379794628 · doi:10.1002/maco.202313765

The anoxic corrosion of mild steel in a cementitious repository environment

2023· article· en· W4379794628 on OpenAlexaff
Nicholas A. Senior, Taylor Martino, Nikitas Diomidis, R. Gaggiano

Bibliographic record

VenueMaterials and Corrosion · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCorrosionAnoxic watersCementitiousCementRadioactive wasteEnvironmental scienceMaterials scienceHydrogenMetallurgyWaste managementEnvironmental chemistryEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract The long‐term disposal of low‐ and intermediate‐level radioactive waste is anticipated to involve the encapsulation of the material within a cementitious matrix before placement in a deep geological repository. After sealing, the waste repository will become anoxic and the corrosion of emplaced metals will result in the production of hydrogen gas. Knowledge of the rate at which this hydrogen is generated is valuable in that it permits the assessment of pressure build‐up within the repository. The corrosion behavior of mild steel has been monitored through the generation of hydrogen gas, under simulated repository conditions that mimic various stages of cement evolution. It was found that, when cement is used to encapsulate steel, the passage of several years is required for the steel and cement to reach a steady hydrogen production rate. Steel that is directly immersed in simulated cement pore water requires only months to stabilize. All measured steel uniform corrosion rates were significantly below 1 nm year −1 at 50°C, regardless of the test environment, after more than 4 years of exposure.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.325

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)0.0000.000

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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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