MétaCan
Menu
Back to cohort
Record W2102674951 · doi:10.5006/1.3278171

Corrosion Fatigue and Near-Neutral pH Stress Corrosion Cracking of Pipeline Steel and the Effect of Hydrogen Sulfide

2005· article· en· W2102674951 on OpenAlexaffabout
R.L. Eadie, K. E. Szklarz, R. Sutherby

Bibliographic record

VenueCORROSION · 2005
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsTransCanada (Canada)Shell (Canada)University of Alberta
Fundersnot available
KeywordsMaterials scienceCorrosionStress corrosion crackingHydrogen sulfideMetallurgyHydrogen embrittlementSulfideCrackingCorrosion fatigueEnvironmental stress fractureHydrogenStress (linguistics)Anaerobic corrosionPipeline (software)Composite materialSulfurChemistryEngineering

Abstract

fetched live from OpenAlex

Crack advance has been studied in an X-70 pipeline steel, using the compliance technique. The electrolyte used in the study was a very dilute brine bubbled with 10% carbon dioxide (CO2). Cracking rates were studied at a range of frequencies and R-values (R is stress ratio). The R-values simulated typical high-pressure gas service, but the frequency used was significantly higher than that found in gas pipelines. Similar testing was carried out with the addition of 1% hydrogen sulfide (H2S) to the electrolyte using the Shell Canada sour gas test facility. Tests also were run to determine the rate of corrosion fatigue in the same solutions. The crack advance observed in dilute brine bubbled with 10% CO2 could be explained using corrosion fatigue and a threshold, ΔK, of 10 MPa√m. There has been some previous work done at lower frequencies in a less dilute but related electrolyte, and these data have been compared to the present results. The effect of 1% H2S was a dramatic increase in the cracking rate at these frequencies.

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)0.001
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.014
GPT teacher head0.270
Teacher spread0.256 · 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

Citations38
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueCORROSIONSame topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207