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Record W2332294537 · doi:10.1061/9780784479414.044

Impact of Extreme Weather on Transmission Line Structures

2015· article· en· W2332294537 on OpenAlexaffabout
Ibrahim Hathout, Karen Callery

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsHydro One (Canada)
Fundersnot available
KeywordsThunderstormTornadoMicroburstMeteorologyComputer scienceTransmission (telecommunications)Extreme weatherEnvironmental scienceWind speedTelecommunicationsGeologyGeographyClimate changeWind shear

Abstract

fetched live from OpenAlex

High Intensity Wind (HIW) associated with tornadoes and microbursts pose a major threat to transmission line networks in Ontario and many other regions around the world. Almost all transmission structures failures in Ontario in the last 15 years can be attributed to HIW, particularly microbursts. However, when assessing the structural adequacy in accordance to current designs codes, the codes assume that synoptic wind profile provides the basis of wind loading in the design process and does not consider the HIW components associated with severe thunderstorms. Such assumptions may be leaving transmission networks exposed to an unquantified level of threat to a meteorological events associated with severe thunderstorms. In this paper, the models for tornadoes and microbursts are discussed and a case study is presented to demonstrate the destructive effects of these extreme wind events. Recommendations for revising the design criteria for extreme weather are given.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.274
Teacher spread0.235 · 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 designObservational
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

Citations2
Published2015
Admission routes2
Has abstractyes

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