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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 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
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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.294

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 communication0.0000.000
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 head0.045
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