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Record W2040357696 · doi:10.1088/0964-1726/22/7/075010

Wireless overhead line temperature sensor based on RF cavity resonance

2013· article· en· W2040357696 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmart Materials and Structures · 2013
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRadio frequencyElectric power transmissionOverhead (engineering)Transmission lineRadio frequency power transmissionConductorElectrical engineeringFeed lineAntenna (radio)Line (geometry)Wireless sensor networkPower (physics)Power transmissionTransmission (telecommunications)WirelessElectronic engineeringMaterials scienceEngineeringTelecommunicationsPhysicsComputer science

Abstract

fetched live from OpenAlex

The importance of maximizing power transfer through overhead transmission lines necessitates the use of dynamic power control to keep transmission line temperatures within acceptable limits. Excessive conductor operating temperatures lead to an increased sag of the transmission line conductor and may reduce their expected life. In this paper, a passive wireless sensor based on a resonant radio frequency (RF) cavity is presented which can be used to measure overhead transmission line temperature. The temperature sensor does not require a power supply and can be easily clamped to the power line with an antenna attached. Changing temperature causes a change of cavity dimensions and a shift in resonant frequency. The resonant frequency of the cavity can be interrogated wirelessly. This temperature sensor has a resolution of 0.07 °C and can be interrogated from distances greater than 4.5 m. The sensor has a deviation from linearity of less than 2 °C.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.941

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)0.0010.000

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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