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Record W3022234491 · doi:10.5006/c2017-09602

Fitness for Purpose of Low Temperature Cure Liquid-Applied Coating Systems for Pipeline Maintenance

2017· article· en· W3022234491 on OpenAlexaboutno aff
Haralampos Tsaprailis, Jiajun Liang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Failure Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline (software)CoatingMaterials sciencePipeline transportCorrosionPreventive maintenanceComputer scienceProcess engineeringForensic engineeringReliability engineeringEngineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Abstract In Canada, CSA Z245.30-14 covers the application of shop and field applied external coating systems for below ground steel pipelines. CSA Z245.30-14 was enacted in 2015 when the latest version of CSA Z662 was published. Similar to ISO 28109-3, the CSA Z245.30-14 outlines the testing criteria that liquid applied epoxy and fusion bonded epoxy coatings shall satisfy (Table 1 in CSA Z245.30-14). Pipeline maintenance and repair work can occur during the winter season in certain northern regions in Canada due to the geographical restrictions placed by the local soil condition (e.g., permafrost). Additional, pipelines transporting compressed or liquid natural gas commodities also have operation temperatures below 10 °C. Subsequently, low temperature cure liquid epoxy coating systems are often used to allow for curing on an operating (i.e., flowing) line. These coating systems allow advertised curing down to as low as -20 degrees Celsius. Many low temperature cure liquid-applied coating products do not comply with the CSA Z245.30-14 standard when tested at the Manufacturer’s advertised maximum rated service temperatures. This paper will discuss the fitness for purpose of low temperature cure liquid-applied coating systems for pipeline maintenance and repair on existing pipelines. Potential revisions to incorporate low temperature cure liquid-applied coating products into Table 1 of CSA Z245.30-14 will be presented along with commentary related to the deviations from the standard.

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)0.003
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.200
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0200.010

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 head0.020
GPT teacher head0.249
Teacher spread0.230 · 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
Published2017
Admission routes1
Has abstractyes

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