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Record W4401557438 · doi:10.2172/2428981

Radiation Aging of Cable Insulation Systems to Support Extension of Cable Electrical Assessment Techniques - CRADA 562 (Final Report)

2024· report· en· W4401557438 on OpenAlexaff
Mark K. Murphy, Maddison Heine, Muthu Elen, Leonard S. Fifield, Lindsay Vasilak, Richard Easterling, David Rouison

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsKinectrics (Canada)
FundersPacific Northwest National LaboratoryOffice of Nuclear EnergyBattelleU.S. Department of Energy
KeywordsExtension (predicate logic)RadiationComputer scienceForensic engineeringEngineeringElectrical engineeringPhysicsOptics

Abstract

fetched live from OpenAlex

Radiation aging (primarily gamma radiation) affects a small subset of the LV cable population based on a 40-year operation. Increasing plant operating life to 60 or 80 years will result in additional cables experiencing degradation, either from radiation or from combined thermal and radiation effects. Previous research work done by EPRI and other researchers focuses on electrical diagnostics of thermal effects. However, the electrical diagnostic response of the cable system to radiation aging is minimally understood. In order to study the electrical responses from XLPE and EPR cabling and correlate these responses with radiation dose and level of thermal exposure, gamma radiation testing was performed at Pacific Northwest National Laboratory (PNNL). This report covers the radiation testing performed at PNNL, which involved irradiation of 30-foot long cables and short witness samples. The cabling was irradiated, at room temperature, using cobalt-60 gamma-rays to intervals of 10 million rads (10 Mrads), for a maximum of 70 Mrads. The results can be used to determine cable conditions (extent of damage) more accurately and localize where the degradation exists along the length of the cable (axial location). These insights can provide operators with the options to focus repair, mitigation, or replacement efforts locally.

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)0.001
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0100.003

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 head0.045
GPT teacher head0.352
Teacher spread0.308 · 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
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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