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Record W152139545 · doi:10.5006/c2000-00139

Fit-for-Purpose Materials Testing for Sour Gas Service

2000· article· en· W152139545 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsSour gasService (business)Materials scienceEngineeringForensic engineeringComputer scienceProcess engineeringWaste managementNatural gasBusiness

Abstract

fetched live from OpenAlex

Abstract Sour environments differ significantly in their aggressiveness to materials depending on such factors as total system pressure, partial pressures of hydrogen sulfide and carbon dioxide, pH of the aqueous phase, temperature, chloride ion concentration, etc. Materials that perform poorly in standard laboratory sulfide stress cracking and hydrogen-induced cracking tests and some field environments may give acceptable performance under other sour service conditions. This paper provides some additional guidance on the manner in which standard sour service testing methods should be selected and performed, and on the interpretation and application of the results. The relevant recommendations and requirements of NACE International (NACE), the European Federation of Corrosion (EFC) and the American Petroleum Institute (API) will be reviewed. The recommendations of NACE, EFC and API for the selection of the most appropriate test method(s), test frequency, and acceptance criteria for particular components and services are listed. Some alternative tests being used to qualify materials for general and specific sour gas service conditions will be described and reviewed.

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)0.000
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: none
Teacher disagreement score0.517
Threshold uncertainty score0.318

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 head0.051
GPT teacher head0.287
Teacher spread0.235 · 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

Quick stats

Citations6
Published2000
Admission routes1
Has abstractyes

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