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Record W2731524789 · doi:10.5006/c2017-09550

Challenges in Providing Effective Cathodic Protection to Thermally Insulated Pipeline Risers

2017· article· en· W2731524789 on OpenAlexaboutno aff
Stephen B. Gibson, Lyndon Crone, Michael Hogarth

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCathodic protectionPipeline (software)Materials scienceCorrosionNuclear engineeringMarine engineeringForensic engineeringMetallurgyElectrical engineeringMechanical engineeringEngineeringElectrodeElectrochemistryChemistry

Abstract

fetched live from OpenAlex

Abstract Cathodic protection (CP) is a method to mitigate external corrosion on buried metallic pipelines. Electrical isolation and electrolyte homogeneity are key factors that influence the performance of the majority of pipeline CP systems. Based on these factors, this paper details specific challenges encountered in the application of CP to thermally insulated pipeline risers in northern Alberta (Canada). The CP issues resulting from the use and installation of thermal insulation, resistance heating (heat trace), and other instrumentation are discussed. Specific pipeline construction practices that increase the probability of these issues occurring are also addressed. Through two typical case studies, the challenges of installation and meeting regulatory requirements are detailed. It is demonstrated that these challenges need to be considered prior to the installation of pipeline risers and associated corrosion protection system(s). Qualified, experienced and competent CP personnel should be directly involved with installations of CP systems, electrical insulation devices and any metallic instrumentation or cladding.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.243
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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