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Record W2611025833 · doi:10.1080/1478422x.2017.1320117

Electrochemical noise monitoring of the atmospheric corrosion of steels: identifying corrosion form using wavelet analysis

2017· article· en· W2611025833 on OpenAlex
Chao Ma, Shizhe Song, Zhiming Gao, Jihui Wang, Wenbin Hu, Yashar Behnamian, Da‐Hai Xia

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.

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of Tianjin CityNational Natural Science Foundation of China
KeywordsElectrochemical noiseCorrosionMaterials scienceMetallurgyElectrochemistryWaveletNoise (video)Atmosphere (unit)ElectrodeMeteorologyChemistry

Abstract

fetched live from OpenAlex

The early stage atmospheric corrosion of T91 and Q235B steels exposed to Tianjin’s urban atmosphere over 20 days was studied using two electrochemical probes via an electrochemical noise (EN) technique. To identify the corrosion process and the corrosion form of the two steels, EN data were analysed by statistics and wavelet transform. The results revealed that the wavelet energy of decomposed EN mainly located at high-frequency level for Q235B steel, whereas it mainly located at the low-frequency level for T91 steel. Analyses of surface images confirmed that Q235B steel underwent uniform corrosion whereas T91 steel suffered from localised corrosion. The obtained noise resistance correlated well with weight loss data.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.121
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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