An advective-dispersive transport model for residential water consumption
Bibliographic record
Abstract
Abstract This work applies an advective-dispersive framework to simulate utility-wide residential water consumption using the analogy of a continuum transport process. In this context, the advective-dispersive process describes how changes in real water price and seasonal weather variability influence water consumption distribution, which ultimately governs mean and total water consumption values. Water consumption response is measured using histogram data optimally fit using parametric probability density functions (PDF) that have consistent parametrization over the entire observation period. Median statistic denotes advection and prescribes location of the measurement-space PDF, while standard deviation combined with standard-score PDF denotes dispersion which provides the measurement-space PDF with scale and shape. Combining location, scale, and shape components produces a measurement-space PDF that represents the solution to advective-dispersive transport phenomena. We use a Taylor series expansion of the statistics that define the PDF along with curvilinear regression to develop constitutive relationships that define how location, scale, and shape of the PDF respond to price and weather information. This results in a fully parametrizing advective-dispersive process represented by a partial differential equation that provides a tool for anticipating the probability that households will experience water poverty or use excess amounts as price, weather, and policy considerations change through time. This approach is conducive to automation when combined with smart water metering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".