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Record W2389185460

Reliability Calibration of Tower Members in Transmission Line

2014· article· en· W2389185460 on OpenAlexaboutno aff
Feng Yunfe

Bibliographic record

VenueElectric Power Construction · 2014
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsWind engineeringTowerReliability (semiconductor)Structural engineeringTransmission lineEngineeringElectric power transmissionTransmission towerWind speedLine (geometry)Electrical engineeringMeteorologyMathematicsPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

To set the reliability level of transmission line crossing high speed railway,the statistical parameters of the load and resistance of tower members in transmission line were analyzed and calculated.The reliability of tower members designed based on the current code were calibrated.It is indicated that the reliability indexes for strength of axially loaded members or the stability of axial compression members are 3.10 and 2.99,respectively,in the combination of permanent load and wind load;and are 3.35 and 3.20 respectively,in the combination of permanent load,wind load and ice load.The reliability of tower member in transmission lines in China is somewhat lower than that of building steel structures,in the combination of permanent load and wind load;but it is close to the reliability of tower members in US,and higher than that in Canada,in the combination of permanent load and wind load,or in the combination of permanent load,wind load and ice load.

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)0.002
metaresearch head score (Gemma)0.011
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.003
GPT teacher head0.177
Teacher spread0.174 · 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
Published2014
Admission routes1
Has abstractyes

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