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Record W126579964 · doi:10.5006/c2000-00773

Coating Trial and Selection of Rehabilitation Coatings for Large Diameter Pipe

2000· article· en· W126579964 on OpenAlexaff
Aida Lopez, Yvanna Ireland, Kevin C. Garrity

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsCoatingMaterials scienceSelection (genetic algorithm)CorrosionMetallurgyComposite materialForensic engineeringEngineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Since 1996, a major North American pipeline company has been conducting large scale pipeline recoating in sections that are known to have coating problems due to coating degradation and/or aging, increasing operating temperatures and soil aggressiveness. In order to choose the best appropriate material, several coatings were put through an application trial on pipe using line-travel plural component spray equipment at a test facility in Houston, Texas. This paper describes the testing matrix that was used to evaluate the performance characteristics of various wet applied pipeline rehabilitation coatings. Attention was given to i) the field and practical application of each respective coating using the line-travel equipment and ii) the evaluation of the results of long term accelerated testing of the selected materials, specifically the cathodic disbondment resistance and other properties considered to be relevant, including impact resistance, abrasion and adhesion. Also testing data is provided from the actual field application that took place during the 1998 project.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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