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Record W2112950344 · doi:10.1109/tpwrd.2007.899772

On-Load Network De-Icer Specification for a Large Transmission Network

2007· article· en· W2112950344 on OpenAlex
Ren Cloutier, Andr Bergeron, Jacques Brochu

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2007
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsElectric power transmissionCircuit breakerTransformerEngineeringElectrical engineeringIcingTransmission networkInstallationTransmission lineElectrical networkElectrical conductorTransmission (telecommunications)VoltageMechanical engineeringMeteorology

Abstract

fetched live from OpenAlex

This paper presents a feasibility study conducted with a view to installing an on-load network de-icer (ONDI) for de-icing HV transmission lines forming part of the Matapedia subtransmission network operated by Hydro-Quebec. The ONDI concept makes use of a phase-shifting transformer (PST) to induce very large ac currents to heat line conductors by the joule effect. A single ONDI can handle a number of lines of different length with no need to transfer loads to other lines or disconnect them. In the study presented, more than 900 km of 230- and 315-kV HV circuits can be de-iced with one ONDI installed at Rimouski substation. To put the ONDI to work, only existing circuit breakers need to be operated so that no open-air (ice-sensitive) disconnecting switches are involved. The adequacy of this planning study is also discussed here with regard to taking into account uncertainties related to the weather conditions expected for severe ice storms.

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)0.001
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: none
Teacher disagreement score0.968
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.228
Teacher spread0.216 · 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