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Record W4405360375 · doi:10.1115/ipc2024-132939

New Specifications for Steel Pipeline Induction and Cold Bends (CSA Z245.16 and Z245.17)

2024· article· en· W4405360375 on OpenAlexaffabout
Pablo Marchi, Fred Myschuk, Su Xu, Hamed Mirabolghasemi

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2024
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsNatural Resources CanadaCanadian Standards Association
Fundersnot available
KeywordsPipeline (software)Computer scienceMaterials scienceStructural engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Steel pipe bends manufactured by induction or cold bending processes have become increasingly prevalent in the oil and gas pipeline industry, often replacing fittings in many applications. However, the absence of clear specifications for the manufacturing of these types of bends in the existing CSA standards prompted the development of new standards. In response, two new standards, CSA Z245.16:22 Induction bends, and CSA Z245.17:22 Cold bends were developed, and were published in August 2022. Notably, the CSA Z245.17:22 Standard, focusing on the manufacturing processes of factory-made cold bends designed primarily for integration into oil or gas pipeline systems, is the first of its kind. This is a significant development in the Canadian oil and gas pipeline industry, complementing the existing specification of CSA Z662 Oil and gas pipeline systems. The main objective of these new standards is to establish clear and consistent requirements for factory-made induction and cold bends, distinct from the existing CSA Z245.11 Steel fittings standard. This differentiation is important because starting material and manufacturing processes qualification differ from those for fittings. The publication of these new bends standards provides clarification on the requirements specific to induction and cold bends. This paper reviews the content and application of CSA Z245.16:22 and CSA Z245.17:22. It discusses the main requirements introduced and outlines a plan for future changes to the standards for their next edition in 2025.

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)0.009
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0160.018

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 head0.028
GPT teacher head0.240
Teacher spread0.213 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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