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Record W2518263483 · doi:10.5006/c2012-01674

Evaluation of FBE Coatings for High Temperature Pipeline Applications

2012· article· en· W2518263483 on OpenAlexaboutno aff
José M. Contreras, Miguel Mateus Barragán, Alban Jaimes Suárez, Miguel Manrique Rojas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline (software)Materials scienceCorrosionMetallurgyEngineering physicsComputer scienceEnvironmental scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract In oilfields from Colombia, the surface operating temperatures of pipelines are around 120°C. Actually, Canadian and ASTM standard tests set the conditions for Cathodic Disbonding Testing to maximum temperature of 95 °C, while our scope for coating evaluation is between 95 to 150 °C. To select for higher performance coatings systems, we have developed a testing protocol to be applied at the factory, in the laboratory and in the field to assess the performance of coatings for operating conditions between 95 to 150°C. The protocol involved the following tests: dry film thickness, porosity, mechanical, abrasion resistance test, impact and elongation, adhesion, wet adhesion, immersion in chemical solution, cathodic disbonding and electrochemical impedance. The results in the laboratory can be correlated between the glass transition temperature and the electrochemical response. To clarify damage level found in the coatings evaluated. As a result of this work was found a reliable methodology for evaluating coatings at temperatures above 95°C. A good correlation was found in the results of electrochemical impedance tests and cathodic disbondment evaluated to 150°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.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.028
GPT teacher head0.260
Teacher spread0.232 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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