Water Quality Assessment in the Application of Stormwater Reuse for Irrigating Public Lands
Bibliographic record
Abstract
Abstract Stormwater reuse for irrigating public lands presents a viable option for reducing potable water demand in urban settings. However, stormwater generally contains high pollutant levels, which may cause adverse effects on public health and the environment. Water quality in a stormwater retention pond in the City of Calgary, Alberta, was examined in order to assess the feasibility of reusing stormwater for irrigation purposes. Field campaigns were conducted in the 2004, 2005, and 2006 irrigation seasons. The water quality data indicated that the pond water quality generally satisfies the requirements for stormwater recycled as irrigation water. Relationships between stormwater quality and climatological variables were investigated using correlation and regression analysis. Their correlations suggest that intermittent rain events contribute to elevated microbial levels and total suspended solids (TSS). Other climatological variables—air temperature, cloud cover, wind speed, and relative humidity—are also correlated with certain water quality parameters including fecal coliform (FC), TSS, nutrients, and conductivity. Formulated regression equations demonstrate good predictions of observed FC and TSS using climatological variables. Results showing stormwater quality as a function of climatological variables imply that climate change might have potential influence on stormwater quality.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".