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Record W2052374430 · doi:10.5006/1.3319131

Microbiologically Influenced Corrosion of Nuclear Waste Containers

2009· article· en· W2052374430 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCORROSION · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsKintama (Canada)Vancouver Island University
Fundersnot available
KeywordsContainer (type theory)Radioactive wasteCorrosionEnvironmental scienceWaste managementLead (geology)Risk analysis (engineering)Forensic engineeringEngineeringBusinessChemistryGeology

Abstract

fetched live from OpenAlex

Microbiologically influenced corrosion (MIC) is one of a number of threats to the long-term integrity of nuclear waste containers. As such, the potential for, and extent of, MIC must be assessed and suitable models developed for predicting the long-term behavior of the container. There are two broad approaches to assessing the threat posed by MIC; first, to determine whether the environment will support microbial activity and, if so, where and when it will occur, and second, to estimate the maximum amount of damage that could occur if microbial activity in the repository is possible. A decision-tree approach is used to present evidence for both of these approaches and to decide whether MIC is a significant threat to the integrity of the container. Examples are provided from various international nuclear waste management programs. It is concluded that microbial effects will not compromise the safety of the overall disposal system because they will not lead to either early container failures or to a large number of simultaneous failures, both factors that can lead to an increase in the peak dose.

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.

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 score0.003
Threshold uncertainty score0.876

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)0.0010.000

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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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