MétaCan
Menu
Back to cohort
Record W1976399685 · doi:10.1115/ipc2004-0368

A Permeable Coating Model for Predicting the Environment at the Pipe Surface Under CP-Compatible Coatings

2004· article· en· W1976399685 on OpenAlexaff
Fraser King, Tom Jack, Miroslav Kolář, Robert Worthingham

Bibliographic record

Venue2004 International Pipeline Conference, Volumes 1, 2, and 3 · 2004
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsTransCanada (Canada)Nova Chemicals (Canada)
Fundersnot available
KeywordsCoatingCorrosionMaterials scienceCathodic protectionElectrochemistryMetallurgyComposite materialEnvironmental scienceElectrode

Abstract

fetched live from OpenAlex

Some underground pipeline coatings, such as asphalt, coal tar enamel and fusion-bonded epoxy, are said to be CP-compatible. When these coatings degrade and groundwater contacts the pipe, the surface is still protected from corrosion and stress corrosion cracking (SCC) as the CP current can pass through the permeable coating. The electrochemical reactions on the pipe surface generate a local environment under the coating that is quite different from that in the surrounding soil. In general, the pH of the trapped water increases due to the cathodic reduction of water and oxygen by the CP current. A mathematical model has been developed to predict the generation and evolution of the environment under a disbonded permeable coating as a consequence of the action of CP. The model couples the electrochemical reactions on the surface of the pipe to the transport of species to and from the pipe surface through the permeable coating and the surrounding soil. The model is structured to use available field data (such as soil and ground water data and information from CP surveys) to predict conditions on the pipe surface. The model can be used to predict CP and environmental conditions under which the pipe may be susceptible to corrosion or SCC.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.031
GPT teacher head0.261
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2004
Admission routes1
Has abstractyes

Explore more

Same venue2004 International Pipeline Conference, Volumes 1, 2, and 3Same topicCorrosion Behavior and InhibitionFrench-language works237,207