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Record W3026886310 · doi:10.5006/c2019-12829

Laboratory Testing of Low Temperature Cure Liquid-Applied Coating Systems for Pipeline Maintenance

2019· article· en· W3026886310 on OpenAlex
Russell Draper, Jiajun Liang, Haralampos Tsaprailis

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
TopicSurface Roughness and Optical Measurements
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsPipeline (software)CoatingMaterials scienceCorrosionPipeline transportProcess engineeringForensic engineeringReliability engineeringNuclear engineeringComputer sciencePetroleum engineeringEngineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Absrract This paper describes the laboratory testing to assess the performance and curing characteristics of low temperature cure liquid-applied coating systems for pipeline maintenance. Four low temperature cure coating systems were applied to abrasively blasted steel under simulated winter field conditions. The steel substrate temperatures were maintained at 0 °C during application and curing up to the point of testing. The low temperature cured coating samples were tested in accordance with industry standards for cathodic disbondment resistance, adhesion before and after hot water immersion, impact testing and flexibility that would be representative of maintenance activities. The curing rates at low temperatures were also determined using differential scanning colorimetry (DSC). Based on the laboratory testing and curing results, it was concluded that selected coating systems were suitable for maintenance application to excavated oil and gas pipelines, when the lines are operating at temperatures as low as 0 °C.

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: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.510

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 head0.014
GPT teacher head0.210
Teacher spread0.197 · 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