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Record W2749130627 · doi:10.1080/1478422x.2017.1303102

Understanding and quantifying the anoxic corrosion of carbon steel in a Swiss L/ILW repository environment

2017· article· en· W2749130627 on OpenAlexaff
Nicholas A. Senior, Roger Newman, Steve Wang, Nikitas Diomidis

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCorrosionGroutAnoxic watersMetallurgyMaterials scienceCementAnaerobic corrosionCarbon steelHydrogenEnvironmental chemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

The anaerobic corrosion of steel was monitored under simulated Swiss low-/intermediate-level waste repository conditions. Steel was studied both as bare rods and cast in cement, in water vapour, simulating the unsaturated anoxic phase, and during immersion in various pore waters, representative of the saturated phase. All tests were performed at 50°C. Hydrogen evolution from grout, comprising commercial cement and sand, was also studied. Grouted steel specimens initially generated hydrogen that was comparable to grout alone, which was attributed to the presence of iron detritus from the ball-milling of cement clinker. The corrosion rate of immersed test specimens peaked during the first 100 days, likely due to the formation of a passive film. Corrosion rates generally declined to <3 nm/year, although there were several notable exceptions where hydrogen generation was erratic. This was attributed to localised corrosion and may be an important factor to understand when modelling hydrogen generation rates.This paper is part of a supplement on the 6th International Workshop on Long-Term Prediction of Corrosion Damage in Nuclear Waste Systems.

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 score0.043
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.027
GPT teacher head0.253
Teacher spread0.225 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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