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Probabilistic Safety Risk Assessment near Transmission Line Structures under Fault Conditions

2024· article· en· W4399940027 on OpenAlexaff
Chenyang Wang, Xiaodong Liang, Manpreet Kaur

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsUniversity of SaskatchewanUniversity of ManitobaManitoba Hydro
Fundersnot available
KeywordsProbabilistic logicReliability engineeringComputer scienceFault (geology)Transmission lineRisk assessmentProbabilistic risk assessmentComputer securityEngineeringArtificial intelligenceTelecommunicationsGeology

Abstract

fetched live from OpenAlex

The grounding system is an essential part of the transmission line system. In the last decade, more transmission lines have been constructed near urban areas that share the land with other public infrastructures, such as commercial lots, parks, playgrounds, walking trails, etc. This co-location raises safety concerns due to touch and step voltage hazards near transmission line structures under power line faulty conditions. In many countries, there is no national standard to tackle this concern and provide clear regulations. To overcome this issue, a probabilistic risk assessment method is utilized in this paper, which serves as a great tool to quantify this risk and make it possible to compare it with other common risks to develop acceptable criteria. A methodology is developed to determine the probability of risk, and each parameter used in the calculation is refined based on historical operational records and monitoring data.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.276
Teacher spread0.268 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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