Measuring Urban Forest Canopy Effects on Stormwater Runoff in Guelph, Ontario
Bibliographic record
Abstract
Cities are complex and contribute to urban stream degradation due to increased stormwater runoff volumes and velocities and ineffective stormwater management infrastructure. Trees provide measurable benefits to cities including rainfall interception which can decrease stormwater runoff, demonstrating their effectiveness as a stormwater management tool. This study quantifies the effects of urban forest canopy on stormwater runoff to assess proportions of canopy cover required to effectively reduce runoff levels. i-Tree Hydro, a semi-distributed hydrological model, was used to measure hydrologic effects of the City of Guelph’s urban forest. Varying proportions of canopy cover were used to compare Guelph’s current and potential urban forest. Results show that increasing canopy cover in plantable spaces decreased overall flow within the City, however, runoff over impervious surfaces increased. The findings can inform design decisions related to urban stormwater management and improve urban forest management measures, however, impervious surfaces remain a design challenge.
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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 | 0.002 | 0.001 |
| 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".