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Record W2528656736 · doi:10.1139/tcsme-2000-0029

ATMOSPHERIC ICING ON A TEST POWER LINE

2000· article· en· W2528656736 on OpenAlexaffvenueabout
J. Druez, M. Farzaneh, Pierre McComber

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Chicoutimi
FundersHort Innovation
KeywordsIcingSnowFreezing rainHard rimeEnvironmental scienceMeteorologyRain and snow mixedRain gaugeIcing conditionsAtmospheric sciencesGeologyPrecipitationPhysics

Abstract

fetched live from OpenAlex

Atmospheric icing is a major factor for power line design and reliability. Therefore, a test power line, located on mount Valin (Québec) Canada, was used to obtain data on atmospheric icing. The most dangerous ice accretions are glaze, hard rime, wet snow and mixtures. Results show it is possible to distinguish between them using temperature measurement, and comparative analysis of the data from an icing rate meter and a heated rain and snow gauge. The ice load variation on conductors and the meteorological data are presented for the most important icing events of the 1991-1992 season. The analysis shows that the icing and shedding rates are generally greater for the 12.5-mm diameter cable than for the 34-mm cable; single or in a bundle. On the average, the shedding rate is higher for freezing rain and wet snow, compared to in-cloud icing. Comparing the mount Valin data with a constant rate ice accretion and shedding model, the correlation coefficient varies from 92 to 97 percent.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

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

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 head0.006
GPT teacher head0.180
Teacher spread0.173 · 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 designBench or experimental
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

Citations4
Published2000
Admission routes3
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicIcing and De-icing TechnologiesFrench-language works237,207