Bibliographic record
Abstract
Abstract There are several issues surrounding the general practice of water demand forecasting. Boland (1) points out that any forecast has two essential components: explanation and prediction: “Explanation of water use usually takes the form of a model that relates the past observed level of water use to various variables. Replacing past values of the explanatory variables with those expected in the future produces a prediction of future water use” (pp. 162–163). Historically, the first of these two tasks—explaining water use—has been done by using ‘fixed‐coefficient’ models. In this approach, it is assumed that water use is related to a single explanatory factor such as population and, further, that the relationship between water use and population is a fixed, proportional one. Dziegielewski (2), for example, provides a brief review of the history of urban water demand forecasting and demonstrates that, in the ‘traditional’ method of forecasting, total future demand is predicted as the product of expected population growth and a fixed per capita water use coefficient. This method was subsequently refined by disaggregating total water use by user classes, area, and time period. The fundamental forecasting method remained the same, however; ‘unit water use coefficients’ is multiplied by the projected growth in a particular user group in a specific location. The fundamental shortcoming of the fixed coefficient approach is that it fails to anticipate changes in the relationship between water use and the dominant explanatory variable that may arise from changes in other, neglected variables. For example, forecasts of residential water use that are based on the number of households in an area may overstate future water demands if they neglect the impacts of rising water prices. More recently, more complex models have sought to estimate statistically the relationship between water use and a set of explanatory variables, including water prices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".