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Record W1991446435 · doi:10.1115/ipc2006-10443

Performance Testing of a Multi-Component Powder Coating System

2006· article· en· W1991446435 on OpenAlexaff
Jenny Been, R. Given, Robert Worthingham

Bibliographic record

VenueVolume 2: Integrity Management; Poster Session; Student Paper Competition · 2006
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsTransCanada (Canada)Nova Chemicals (Canada)
Fundersnot available
KeywordsCoatingCathodic protectionMaterials scienceLayer (electronics)CorrosionComposite materialElectrochemistryChemistryElectrode

Abstract

fetched live from OpenAlex

A number of new coatings offer the possibility for cost savings and/or improved performance. As a shielding coating, the resistance of a high-integrity three-layer coating to disbondment in damaged areas and as a function of applied cathodic potential is of interest with regard to the creation of an environment on the pipe that will support corrosion and/or cracking. The cathodic disbondment (CD) behavior of a multi-component powder coating system has been characterized under simulated field conditions (restrictive mass transport using soils), rather than the standard CSA protocol. The as-received coating displayed excellent impedance properties and minimal disbondment under normal operating conditions. Coated panels were furthermore subjected to impact damage and a hot water soak prior to the CD tests. Results showed minimal disbondment of the multi-component powder coating system over 3 to 6 months regardless of impact damage or temperature. There was some evidence that more negative potentials may increase the disbondment area over longer periods of time. However, mechanistic considerations, literature information, experimental observations and field experiences would suggest that coating disbondment might be limited when the coating is properly applied and otherwise in good condition. Longer-term experiments are required to confirm the presence of a maximum size disbondment.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.215
Teacher spread0.204 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

Explore more

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