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Record W4232646332 · doi:10.1520/stp48754s

Measurement of Corrosion Potentials of the Internal Surface of Operating High-Pressure Oil and Gas Pipelines

2009· book-chapter· en· W4232646332 on OpenAlexaff
Alebachew Demoz, Sankara Papavinasam, Kirk H. Michaelian, R. Winston Revie

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicEngineering Diagnostics and Reliability
Canadian institutionsNatural Resources CanadaDevon Energy (Canada)
Fundersnot available
KeywordsCorrosionPetroleum engineeringPipeline transportMaterials scienceEnvironmental scienceMetallurgyGeologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Corrosion potential is a fundamental electrochemical parameter critical to understanding the thermodynamic aspects of corrosion. Many corrosion theories are based on corrosion potentials. Corrosion potential is routinely and easily measured in the laboratory. In order to transfer this scientific knowledge from the laboratory to the field, it is necessary to determine the corrosion potential under operating field conditions. In this paper, the unique challenges measuring corrosion potentials in an industrial environment are described using measurements of the corrosion potentials of the internal surfaces of pipelines as examples.

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)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.008
GPT teacher head0.184
Teacher spread0.176 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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