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Record W2321041438 · doi:10.1061/9780784479360.083

Developing an Inline Pipe Wall Screening Tool for Assessing and Managing Metallic Pipe

2015· article· en· W2321041438 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePipelines 2015 · 2015
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsBP (Canada)
Fundersnot available
KeywordsMaterials scienceForensic engineeringEngineering

Abstract

fetched live from OpenAlex

Recent developments in inspection techniques/technologies now make it possible to collect condition data for the entire length of pipeline that can then be evaluated with analytical and engineering techniques to provide a targeted strategy of repair, replacement and management. One specific research and development effort of inline screening technologies began with field trials as part of a 2008 EPA study on innovative condition assessment technologies for water mains. The initial phase of the development of pipe wall assessment (PWA) tools used acoustic pulse technology in qualitative manner to assess the wall strength of a pipeline by determining the change in hoop stiffness over short intervals. On a parallel path, a second PWA technology was developed that measures the change in the self-generated magnetic field produced by ferromagnetic materials in stress. This paper will discuss the development of both technologies.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.612
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.089
GPT teacher head0.333
Teacher spread0.244 · 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