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Record W2062842749 · doi:10.1115/ipc2010-31164

Long Term Corrosion Behaviour of Carbon Steel During Anaerobic to Aerobic Cycling Under Near-Neutral pH Saline Conditions

2010· article· en· W2062842749 on OpenAlexaff
Brent W.A. Sherar, Peter Keech, Zack Qin, Fraser King, David W. Shoesmith, Robert Worthingham

Bibliographic record

Venue2010 8th International Pipeline Conference, Volume 1 · 2010
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsTransCanada (Canada)Western University
Fundersnot available
KeywordsCorrosionMaterials scienceCarbon steelPolarization (electrochemistry)MetallurgyAnaerobic exerciseElectrochemistryRaman spectroscopyCyclingElectrodeChemistry

Abstract

fetched live from OpenAlex

This paper investigates the long term corrosion behaviour of pretreated carbon steel under alternating anaerobic to aerobic cycles over 238 days. Changes in steel behaviour were observed electrochemically by monitoring the corrosion potential, and calculating changes to corrosion rate from linear polarization resistance. With increasing cycle number the corrosion process becomes localized at a small number of locations, consistent with the formation of tubercles. Periods of aerobic corrosion were associated with more positive potentials (between −500 mV to −350 mV) and high corrosion rates (70 to 120 μm yr−1); whereas anaerobic corrosion yielded more negative potentials (< −650 mV) and lower corrosion rates (40 to 50 μm yr−1). Upon termination of the experiment, corrosion product deposits were characterized by several techniques: scanning electrochemical microscopy to detect morphology; focused ion beam and cross sectioning to judge film thickness and film porosity; and Raman Spectroscopy to identify iron phases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 head0.020
GPT teacher head0.282
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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
Published2010
Admission routes1
Has abstractyes

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Same venue2010 8th International Pipeline Conference, Volume 1Same topicCorrosion Behavior and InhibitionFrench-language works237,207