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Record W2749719966 · doi:10.5006/2471

Corrosion of Copper as a Nuclear Waste Container Material in Simulated Anoxic Granitic Groundwater

2017· article· en· W2749719966 on OpenAlexaboutno aff
Xihua He, Tae M. Ahn, Jin‐Ping Gwo

Bibliographic record

VenueCORROSION · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsCorrosionAnoxic watersGroundwaterAutoclaveRadioactive wasteContext (archaeology)Anaerobic corrosionMetallurgyMaterials scienceEnvironmental scienceGeologyEnvironmental chemistryNuclear chemistryChemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

Copper (Cu) is a candidate material for waste packages in geological disposal systems for high-level radioactive waste in Switzerland, Sweden, Finland, Japan, and Canada. This paper reports experimental tests of Cu in the context of radioactive waste disposal applications. Experimental tests of Cu general corrosion and hydrogen evolution were conducted under anoxic conditions (less than 10 ppb of O2) using synthetic saline groundwater based on reference compositions of deep groundwaters in crystalline rock of the Canadian Shield. The results indicate that the Cu open-circuit potential and corrosion rates in anoxic waters were very sensitive to the residual O2 concentration in solution. The corrosion rates ranged from submicrometer to micrometer per year, depending on the residual O2 concentration level. The corrosion products were predominantly cuprous oxide (Cu2O). Chlorine was present in corrosion products for tests exposed to synthetic saline groundwater, but more work is needed to assess its role in the corrosion process. Minute amounts of hydrogen were detected from the autoclave as test cell, however, they cannot be simply correlated to Cu corrosion because of complication of the autoclave material corrosion.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.269
Teacher spread0.250 · 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 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

Citations10
Published2017
Admission routes1
Has abstractyes

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