Bibliographic record
Abstract
The intent of this research was to gain a better understanding of the effects of the design parameters on the thermal impact of stormwater management wet ponds. The effect of upland areas on inlet water temperatures of the ponds, thermal design modeling of the ponds, and cooling trenches effects to mitigate stormwater ponds are investigated using data collected from six stormwater ponds in the cities of Guelph and Kitchener, Ontario, Canada. The sensitivity analyses of the developed predictive artificial neural network (ANN) model showed that the rainfall event mean temperatures significantly influenced stormwater temperatures at the inlet of the ponds. The longest pipe length and pipe network density are the two parameters that control the cooling effect of the underground storm sewer system, as opposed to the impervious percentage of the catchment. Concerning the key design parameters of stormwater ponds, larger permanent pool volumes tend to release the warmer water resident in the ponds. Increasing travel path ratio using baffles can lead to less mixing of the water that is resident in the pond with the cooler fresh event runoff and therefore an increase in event mean temperature of outlet. Increasing pond volume from 2000 to 4000 m³ - while keeping all other parameters constant - results in an average increase of 5 °C in event mean temperature at the pond outlet (EMTO); increasing travel path ratio from 0.6 to 1.2 leads to an average increase of 6 °C in EMTO. Regarding the design parameters for stormwater ponds' cooling trench/ rock crib, the results obtained from the sensitivity analyses of the ANN model revealed that the effect of a cooling trench is significantly influenced by the initial temperature of the water and rock in the cooling trench and influent temperature of the water. Reducing the hydraulic depth from 0.8 to 0.3 m in the model shows a 2 °C improvement in the stormwater runoff cooling efficiency of the trench. Increasing the length of the trench from 50 to 100 m in the model confirms a 3 °C improvement in the stormwater runoff cooling efficiency of the trench.
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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 | 0.000 |
| 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) | 0.002 | 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".