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Record W2172181642 · doi:10.1109/tpwrd.2007.905558

Effects of Surface Decay on Remaining Strength of Transmission-Line Wood Cross-arms

2008· article· en· W2172181642 on OpenAlexaff
Vickie W. K. Ho, Mahesh D. Pandey, Sanjeev Bedi

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2008
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBendingLine (geometry)Transmission lineMaterials scienceEnvironmental scienceComposite materialEngineeringMathematicsElectrical engineeringGeometry

Abstract

fetched live from OpenAlex

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper develops a model that correlates the amount of surface deterioration to the remaining strength of rectangular Douglas-fir cross-arms used in a 115-kV transmission line. One challenge that utility companies face today is to develop effective strategies to replace cross-arms that are potentially detrimental to the transmission-line system. Inservice cross-arms are continuously exposed to the environment and experience wide ranging temperatures and moisture conditions. Furthermore, rainwater entrapment on surface and bolt holes causes rotting and decay of wood, resulting in gradual loss of strength. The evaluation of the remaining strength of inservice cross-arms is complicated further by a large variability associated with the wood microstructure, the decay process, defects, and environmental conditions. To understand the effects of surface decay on the strength of wood cross-arms, an image-processing technique is employed to accurately quantify the amount of deterioration and examine the effects of decay location. Full-scale bending tests are used to assess the remaining strength. Statistical models have shown that surface decay, depending on its location, has a direct linear relationship with the strength loss for wood cross-arms in bending. </para>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.742

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2008
Admission routes1
Has abstractyes

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