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Record W2045092222 · doi:10.1061/40994(321)79

Assessing Performance of a Water Transmission System Using an Inverse Transient Method

2008· article· en· W2045092222 on OpenAlexaffabout
Bryan Karney, Anthony Parente, Eppo Eerkes, C M WHITE

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsRegional Municipality of OttawaResearch CanadaBrock UniversityUniversity of Toronto
Fundersnot available
KeywordsTransient (computer programming)Computer scienceGeneral partnershipTransient responseEngineeringEnvironmental scienceElectrical engineeringBusiness

Abstract

fetched live from OpenAlex

Traditionally, transient pressures have been considered as a potentially destructive influence in systems, possibly leading to pipe or equipment failures and representing a threat to both water quality and smooth operation. More recently it has been realized that transient pressures also carry considerable information about system state and condition. This has lead to so-called inverse transient methods, where a transient signal is used to infer system characteristics and parameters. The current work goes further than even this, specifically by considering the possibility of permanent installations to monitor and assess the system's transient response. This paper describes a collaboration between the Regional Municipality of Peel, the University of Toronto, Earth Tech consultants, and the Pressure Pipe Inspection Company to bring this transient data into focus and to greatly magnify and explore its value. While the final verdict is not yet out, the overall performance of this monitoring system, initial indications are that fruitful and economic partnership between data, sensors and monitoring technologies is possible.

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.255

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 communication0.0000.001
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 head0.039
GPT teacher head0.245
Teacher spread0.207 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2008
Admission routes2
Has abstractyes

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