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Record W2325852132 · doi:10.1061/40927(243)512

Temporal and Spatial Scaling of Instantaneous Residential Water Demand for Network Analysis

2007· article· en· W2325852132 on OpenAlex
Yves Filion, Z. Li, Steven G. Buchberger

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

VenueWorld Environmental and Water Resources Congress 2007 · 2007
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsNode (physics)ScalingVariance (accounting)Computer scienceRange (aeronautics)Space timeNetwork analysisNetwork modelStatisticsSimulationReal-time computingMathematicsMathematical optimizationData miningEngineering

Abstract

fetched live from OpenAlex

The paper presented a stochastic model that simulates time-averaged water demands at network nodes in a skeletonized water distribution system. The model comprises analytical expressions that scale the statistical moments of water pulse data to higher time and space resolutions. The new model was tested against simulated water demand data generated with the PRPsym. The preliminary results indicate that the model accurately scales the mean and variance of time-averaged nodal demand at time steps that range from 30 min to 2 h. Increasing the number of single-family homes connected to a network node does not introduce errors in the mean and variance of time-averaged nodal demand. Numerical testing indicated that time-averaged nodal demands are normally distributed when more than 100 single-family residences are connected to a network node. More data and analysis is needed to confirm these preliminary findings.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.475

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 head0.004
GPT teacher head0.171
Teacher spread0.168 · 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