An analysis of residential water demand for Ontario and the Prairie Provinces from 1989 to 1999
Bibliographic record
Abstract
The research centred on the factors accounting for variations within residential water demand. To accomplish this task, the statistical technique of regression analysis was used to analyze water demand data compiled by Environment Canada, for five different years, within two Canadian regions: Ontario and the Prairie Provinces. Two dependent variables were analyzed: total residential water demand (TRWD) and residential water demand per capita (RWD/cap). Within the TRWD model, population served by water services was found to be the only statistically significant variable, with coefficients of determination around 0.9. It was therefore found, that for forecasting of regional water demands, multiplying a predicted population level with a per capita coefficient might be a sufficient technique at the regional level. Other variables tested included price structure, price of water services, household income and rainfall. With respect to the RWD/cap model, marginal prices were found to be statistically significant within the Prairie region, as well as for the combination of both regions. The multiple linear regression model produced R2 values that were very low (varying from 0.030 to 0.130). (Abstract shortened by UMI.)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".