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Record W2435636849 · doi:10.5006/c2016-07587

Repair Prioritization Analysis for Cased Pipeline Crossings

2016· article· en· W2435636849 on OpenAlexaboutno aff
Alfonso Garcia, Len J. Krissa, Jerry DeWitt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsPrioritizationPipeline (software)Computer scienceCorrosionReliability engineeringForensic engineeringEngineeringConstruction engineeringMaterials scienceMetallurgyOperating system

Abstract

fetched live from OpenAlex

Abstract A major pipeline Company has an inventory of approximately 2300 cased crossings throughout the various regions of Canada and the USA. Emphasis for managing and controlling corrosion within cased pipeline crossings is increasing from both operator and regulatory perspectives. Understanding the causes and characteristics of carrier pipe corrosion is an important stride towards improved integrity management of cased crossings. An excavation at a cased highway location is a complicated and intrusive process considering the impact to traffic and the numerous permits required prior to initiating any repair activity. Execution of repair activities under these circumstances is also very expensive and time constrained. The pipeline industry has recognized these challenges and responded with a proactive solution to prevent situations of this nature. A vapor phase corrosion inhibitor gel solution is being applied to control the corrosiveness of the environment within the annular space of the casing and its effectiveness is continually monitored using remote telecommunication technologies. The technique is very effective on a case by case basis; however due to the number of casings within the system, it becomes impractical to qualify the entire inventory. Subsequently, a prioritization method has been developed to select cased crossings that require immediate mitigation and also schedule long range planning for repairs. The innovative and systematic process evaluates critical information and attributes within an expert environment using established decision making techniques. Priority for all locations is determined by structuring a hierarchy of criteria and eliciting technical judgment of company’s Subject Matter Experts (SMEs), stakeholders, and unbiased industry specialists. Experts’ opinions are supported by combining Cathodic Protection (CP) and Inline Inspections (ILI) results within a structured, multi-criteria decision making matrix to create an enterprise listing for the casing management program.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0120.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.216
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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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