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Record W2126820919 · doi:10.1061/9780784479360.075

Metrics for the Rapid Assessment of Transient Severity in Pipelines

2015· article· en· W2126820919 on OpenAlexaff
Bryan Karney, Ahmad Malekpour, J. D. Nault

Bibliographic record

VenuePipelines 2015 · 2015
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsHydraTek (Canada)University of Toronto
Fundersnot available
KeywordsTransient (computer programming)Transient flowMomentum (technical analysis)Energy (signal processing)Computer sciencePipeline transportKey (lock)Energy–momentum relationFlow (mathematics)Fluid mechanicsProperty (philosophy)Statistical physicsMechanicsIndustrial engineeringPhysicsMechanical engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Concepts relating to energy transformations within built and natural systems have been some of the most fruitful in the history of science and engineering. The property of energy summarizes essential changes both in a system’s state and key interactions with its environment. Traditional unsteady flow analyses, based on momentum and continuity relations, have been dominated by considerations of wave mechanics, such as unsteady fluid friction which is typically accommodated via adjustments to the momentum equation. The current paper demonstrates how conventional analyses can be supplemented with metrics that can provide a complementary understanding of transient flows. Specifically, this study considers the classical Joukowsky equation, mass oscillations, and the role of energy in analyzing the performance of transient protection devices. The goal is to gain insight by considering energy transformations and interactions.

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)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.004
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.290
Teacher spread0.246 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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