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Record W2090113166 · doi:10.1016/j.proeng.2014.11.443

An Energy Approach to Studying Pipe Network Transients

2014· article· en· W2090113166 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

VenueProcedia Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransient (computer programming)Pipeline (software)Energy (signal processing)Perspective (graphical)Computer scienceSystems engineeringSelection (genetic algorithm)Risk analysis (engineering)EngineeringMechanical engineeringArtificial intelligencePhysicsBusiness

Abstract

fetched live from OpenAlex

The current study discusses the benefits of combining an integrated, system-wide energy approach with conventional transient analyses from the perspective of anticipating, modelling, understanding, and mitigating the negative impacts of transient flows. A case study comprising a pumping pipeline demonstrates how an energy approach provides additional perspective and leads to a better understanding of the underlying transient behavior, and understanding that can lead to a more efficient selection of protection strategy. This study is nicely complementary to conventional approaches, and highlights the insights and checks that are sometimes available by using an energy approach to track system transformations and system states.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.776

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 science0.0000.000
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 head0.006
GPT teacher head0.157
Teacher spread0.151 · 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