MétaCan
Menu
Back to cohort
Record W263738382 · doi:10.5006/c2008-08027

Insights into Atlas Cell Testing for Selection of Linings for Oil and Gas Production Vessels and Tanks

2008· article· en· W263738382 on OpenAlexaff
Linda G.S. Gray, Nicole DeVarennes, Blake Bloor, Michael W. O'Donoghue, Ron Graham, Ron Garrett, Vijay Datta, Bob Franke

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Diagnostics and Reliability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsAtlas (anatomy)Selection (genetic algorithm)Petroleum engineeringProduction (economics)Oil productionEnvironmental scienceForensic engineeringMarine engineeringEngineeringComputer scienceGeologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The Atlas cell test (NACE TM0174) and the autoclave test (NACE TM0185) are laboratory tests that are regularly used to select internal linings for vessels and tanks used in processing oil and gas production fluids. The results from the two tests are often quite different and in some notable instances the correlation between Atlas cell test results and field performance is poor. This paper documents our investigation of the various operational details of the Atlas cell test and their influence on the nature of the cold wall effect created in the Atlas cell. A number of commonly used tank linings were evaluated under Atlas cell conditions. The results are used to offer an explanation regarding some of the contradictory behaviour of coatings in Atlas testing, autoclave testing, and field performance.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

Same topicEngineering Diagnostics and ReliabilityFrench-language works237,207