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Record W2997534922 · doi:10.1149/2.0132003jes

Review—Electrochemical Probes and Sensors Designed for Time-Dependent Atmospheric Corrosion Monitoring: Fundamentals, Progress, and Challenges

2019· article· en· W2997534922 on OpenAlex
Da‐Hai Xia, Shizhe Song, Zhenbo Qin, Wenbin Hu, Yashar Behnamian

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

VenueJournal of The Electrochemical Society · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectrochemical noiseCorrosionDielectric spectroscopyPolarization (electrochemistry)Materials scienceElectrolyteElectrodeCorrosion monitoringElectrochemistryNanotechnologyMetallurgyChemistry

Abstract

fetched live from OpenAlex

Electrochemical probes and sensors have been developed to detect and monitor atmospheric corrosion of metallic materials in the past 40 decades. Depending on the measurement methods, the electrodes and structures of probes and sensors can be different. Various mathematical methods and models have been developed to determine the time-dependent corrosion rate of metal under thin electrolyte film. Polarization techniques such as electrochemical impedance spectroscopy (EIS) and linear polarization resistance (LRP) have the advantage of easy data interpretation but have a tendency to interfere with the corrosion system under investigation. Nonpolarized techniques such as electrochemical noise (EN) do not disturb the corrosion system but data interpretation can be problematic. To achieve long term and reliable corrosion monitoring, optimized electrode design and a multichannel electrochemical instrument are required. New corrosion models and novel data interpretation methods are needed in future work.

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)0.001
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 score0.020
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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