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Record W2038300028 · doi:10.1115/ipc2008-64611

The Importance of “Significant” SCC Data Reported to the National Energy Board: An Update

2008· article· en· W2038300028 on OpenAlex
Joe Paviglianiti, Alan Murray, J. Phil Harrison

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsCanada Energy Regulator
Fundersnot available
KeywordsPipeline transportProduct (mathematics)BusinessPipeline (software)On boardEngineeringComputer scienceForensic engineeringAccountingMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

The National Energy Board (NEB) held an Inquiry in 1995 to determine the extent of knowledge and occurrence of SCC on Canadian oil and gas pipelines. The Report of the Inquiry, which was published in December 1996, issued 27 recommendations to promote public safety by encouraging the sharing of information on the extent of SCC and methods for managing and mitigating it. A major recommendation of the Report stated “that the NEB requires companies to report immediately to the NEB any finding of “significant” SCC and any immediate mitigative actions taken...” The definition of “significant” SCC is based on the definition adopted by the Canadian Energy Pipeline Association (CEPA) at the time of the Inquiry. Subsequently the NEB has required companies to submit this information and has used it to monitor the extent of and management of “significant” SCC on their regulated pipelines. This paper examines trends identified from the over 500 “significant” SCC reports submitted to the NEB. The analysis examines trends associated with product shipped, coating type, pipe grade, year of manufacture and SCC location on the pipe. In addition the paper will highlight the length and depth of “significant” SCC features, their methods of detection and the mitigation steps used to reduce any threat posed by the SCC. From the information presented in the paper, companies and regulators should be able to compare their “significant” SCC findings with the NEB average and in so doing aid in the continued management of 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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.049
GPT teacher head0.274
Teacher spread0.225 · 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