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Record W3039624139 · doi:10.1002/maco.202011784

Probabilistic model for pitting of copper canisters

2020· article· en· W3039624139 on OpenAlex
Scott Briggs, Christina Lilja, Fraser King

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

VenueMaterials and Corrosion · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsNuclear Waste Management Organization
Fundersnot available
KeywordsPitting corrosionCorrosionCopperBentoniteProbabilistic logicEnvironmental scienceErosion corrosion of copper water tubesSaturation (graph theory)Monte Carlo methodMaterials scienceMetallurgyGeotechnical engineeringMathematicsGeologyStatistics

Abstract

fetched live from OpenAlex

Abstract Copper canisters for the final disposal of spent nuclear fuel are expected to be subject to general (uniform) corrosion and a limited degree of localised damage. Although the canister is expected to be exposed to predominantly active conditions, it is nevertheless prudent to assess the probability of pitting corrosion in passive conditions. A probabilistic pitting model is developed for aerobic conditions in water‐saturated bentonite. While aerobic, saturated conditions may be unlikely to occur if saturation is slow or O 2 consumption is fast, these conditions have been assumed as a starting point because of the availability of data. The model samples from environmental conditions use a Monte Carlo approach to predict active or passive conditions. Under passive conditions, the model compares corrosion potentials to breakdown and repassivation potentials to determine pit initiations and deaths, respectively. Pit depth distributions can then be calculated. For the assumed conditions, the model predicts that active conditions dominate 90% of the time while the remaining passive conditions can lead to pitting during the first few years primarily controlled by the availability of oxygen.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.291

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 head0.027
GPT teacher head0.217
Teacher spread0.190 · 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