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Record W4308873849 · doi:10.3390/cmd3040035

Degradation of Steel Wires in Bimetallic Aluminum–Steel Conductors Exposed to Severe Corrosion Conditions

2022· article· en· W4308873849 on OpenAlexafffund
Alan Rondineau, Laurent Gaillet, Lamine Dieng, Sébastien Langlois

Bibliographic record

VenueCorrosion and Materials Degradation · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversité de Sherbrooke
FundersLa Rochelle UniversitéUniversité de Sherbrooke
KeywordsGalvanizationMaterials scienceCorrosionMetallurgyScanning electron microscopeGalvanic cellAluminiumComposite materialGalvanic corrosionLayer (electronics)

Abstract

fetched live from OpenAlex

High-voltage electrical cables are prone to saline corrosion, mostly in coastal environments. Steel wires are a crucial element in withstanding the mechanical solicitations of commonly used aluminum conductor steel reinforced (ACSR) cables. An experimental accelerated corrosion test was made, using salt spray tests on greased and ungreased ACSR cables and individual galvanized steel wires. The corrosion mechanism occurring on the specimens was observed by optical microscopy for several durations of corrosion, to determine the evolution of the galvanic layer and steel substrate degradation. This study was completed by an SEM (Scanning Electron Microscopy) and Raman spectroscopy analysis to characterize the corrosion products occurring on the galvanized steel wires. An estimation of the evolution of the mean zinc thickness loss is also given, for each type of specimen. It is shown that the loss rate of the zinc layer is significantly reduced by the presence of aluminum layers around the steel wires and by the effect of the grease. Tensile tests were made on the exposed galvanized steel wires which led to fracture surface observations to assess the effect of corrosion on the evolution of mechanical properties.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.031
Threshold uncertainty score1.000

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 head0.023
GPT teacher head0.238
Teacher spread0.215 · 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 teacher head, not a consensus.

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
Published2022
Admission routes2
Has abstractyes

Explore more

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