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Record W3205600888 · doi:10.1134/s1023193521050141

Development of a Novel Process of Corrosion Rate Estimation of Steel under Stray Current Interference: Q235A Pipe Steel as an Example

2021· article· en· W3205600888 on OpenAlexaff
Chengtao Wang, Wei Li, Yuqiao Wang, Xuefeng Yang, Shaoyi Xu

Bibliographic record

VenueRussian Journal of Electrochemistry · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStray voltageCorrosionPipeline transportInterference (communication)Fast Fourier transformMaterials scienceSIGNAL (programming language)Current (fluid)Noise (video)Electrochemical noiseMetallurgyEnvironmental scienceEngineeringComputer scienceElectrodeElectrical engineeringChemistryElectrochemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract With the progress of urbanization, stray current corrosion has attracted numerous attention due to its safety threaten to buried metallic pipelines around the urban rail transit system, especially to gas pipelines. A method to monitor the corrosion rate of the gas pipeline is urgently needed in view of the existing indirect method of the long-life reference electrode. In this work, a novel process model of corrosion rate estimation steel under stray current interference was proposed. The electrochemical noise (EN) signal was studied in the frequency domain through combined analysis in terms of fast Fourier transform (FFT) and discrete wavelet transform (DWT). According to the energy value of each crystal, the active energy was proposed as a parameter for the estimation of the corrosion rate under the excitation of stray current. The results showed that there is a negative correlation relationship between corrosion rate rcorr and active energy Ea. Thus, the active crystal energy of EN signal may be applied as the possible monitoring method of corrosion hazard during the stray current corrosion process.

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 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 score0.045
Threshold uncertainty score0.554

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)0.0000.000

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 head0.037
GPT teacher head0.312
Teacher spread0.275 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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