Winter Snowpack Accumulation and Stormwater Water Quality Monitoring for Extensive Green Roof Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In North America, the adoption of green roofs (GRs) has been primarily driven by benefits such as stormwater management that they provide in the growing season. However, a large part of the continent, especially Canada, experiences long winters during which vegetation is dormant and the substrate is frozen. As climate change progressively impacts winter precipitation characteristics, these ground conditions contribute to an increased probability of winter floods. It is important to identify how these green stormwater systems operate year round to maximize their execution as climate adaptation solutions. This study evaluated snowpack accumulation and stormwater quality for extensive GRs that vary in vegetation type and biochar amendment. Snow depth and density measurements were collected over two winter seasons to observe the change in snow cover based on vegetation type. Vegetation analysis concluded that native plant mix contributed to significantly greater snow depths than sedum, but overall snow accumulation of both the native and the sedum GR testbeds was similar to a conventional roof. Green roof leachate samples were collected and analyzed for pH, electrical conductivity, total solids, total suspended solids, and total dissolved solids. These results were compared across GR treatments, as well as to a conventional roof membrane and undisturbed snow samples. Green roof testbeds that were partially covered with native plants had similar total solids to the testbeds with full sedum coverage, both of which were greater than the control samples. The addition of biochar did not significantly alter discharge water quality. This work demonstrated that the type of vegetation used for GR systems and its cover density potentially impacts snow accumulation. Additionally, despite the long periods of frozen growing media, these systems continue to leach pollutants through the winter seasons.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 it