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Record W2315073404 · doi:10.1115/pvp2003-2079

A Review of Wear Scar Patterns of Nuclear Power Plant Components

2003· review· en· W2315073404 on OpenAlexaff
Pak Lim Ko, Agne`s Lina, Antoine Ambard

Bibliographic record

Venuenot available
Typereview
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsBC Innovation Council
Fundersnot available
KeywordsHeat exchangerNuclear power plantMaterials scienceErosion corrosionComponent (thermodynamics)CavitationCorrosionForensic engineeringMechanism (biology)InletComposite materialMetallurgyMechanical engineeringEngineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

To date, almost all the studies related to component damage have been concerned primarily with dynamic interactions at the interface of contacting components and the subsequent damage due to mechanical wear. Based on the results of examination of a large assortment of photo-micrographs taken from worn reactor components and worn specimens from a broad range of test facilities, it appears that, in many cases, mechanical wear is only a secondary contributing mechanism. With the exception of special cases where severe flow-induced vibration might have occurred, such as in some condensers and primary heat exchangers as well as in the U-bend and inlet regions of some earlier steam generators, resulting in severe component interactions causing substantial wear damage, erosion, corrosion, impacting and perhaps cavitation would seem to be the primary contributing mechanisms.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.840
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0030.000

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 head0.044
GPT teacher head0.268
Teacher spread0.224 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2003
Admission routes1
Has abstractyes

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