Effects of Biochar on Green Roofs' Hydrology in a Southern Ontario Climate
Bibliographic record
Abstract
Wide implementation of green roofs throughout urban areas has been carried out across the globe and has the potential to reduce and attenuate volume and peak discharge caused by natural precipitation. Research shows that implementing green roofs in urban environments can be a feasible solution to manage stormwater more effectively, alleviating the capacity compromise of conventional combined sewer overflows systems, as well as other ecological and socio-economic benefits. This study evaluated the hydrologic performance from eight extensive green roof testbeds with a substrate depth of 15 cm, two types of soil media (amended with biochar and control), and two types of vegetation (Sedum and native species) at the University of Toronto’s GRITlab 2 (Spadina) between April and August of 2021. This short-term study showed an overall good performance on water retention and peak attenuation, mainly through evapotranspiration (ET) throughout the monitoring period.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".