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Record W3094179201

Comparison of parameters in the formation of corrosive sulphide deposition on copper conductors

2020· preprint· en· W3094179201 on OpenAlexaff
Janvier Sylvestre N’cho, I. Fofana, Fransisco Kouadio Konan, Abderrahmane Béroual

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2020
Typepreprint
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsCopperElectrical conductorDeposition (geology)MetallurgyMaterials scienceComposite materialGeology
DOInot available

Abstract

fetched live from OpenAlex

Copper is a catalyst that promotes the formation of corrosive sulphur but it is also corroded by the latter. Corrosive sulfur in oil has been identified as the cause of recent failures in power transformers and shunt reactors. The most common reason of such failures is arcing between adjacent disks or conductors of windings due to the formation of copper sulphide deposition on cellulosic insulating paper. Synergetic effects with temperature, time and oxygen are recognized to play a role in the formation of corrosive sulphur. Which of these factors has the most impact on copper sulphide deposition? To address this concern, a quantitative laboratory technique has been developed. It is shown that by using a series of laboratory experiments in accordance with ASTM D 1275 B, it is possible to investigate this problem and to map the influence of these parameters by manipulating some variables such as oxygen, temperature and time on the copper samples. The obtained results show that temperature is the most influential parameter in the formation of corrosive sulphur. The process is accelerated when both temperature and time act conjointly.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.437
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.273
Teacher spread0.233 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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