Plant cover and biomass change on extensive green roofs over a decade and ten lessons learned
Bibliographic record
Abstract
Green roofs are well studied for the environmental, social, and economic services these provide. As a result, green roofs are widespread and within the common vernacular of city residents. Green roof bylaws and construction standards are present in many cities in North America, rooting the presence of this green infrastructure within urban landscapes. Although examples of green roofs constructed decades ago exist, rarely are green roofs monitored over such long periods, and in ways that allow for experimentation, analysis, and conclusions about performance or function. In this study we present findings on plant cover and biomass from a green roof testing facility in Toronto, Canada that was monitored for over a decade. We examine the contributions of growing media, planting, and irrigation in the first seven years (2011-2021) of the eleven-year monitoring period. We found that during this maintenance phase period (2011-2017), plant cover and biomass was highest in modules planted with Sedum, included organic media, and were irrigated, whereas non-irrigated modules planted with forbs and grasses had the poorest performance regardless of media type. Following the stoppage of irrigation, and the post maintenance phase (2017-2021), modules initially planted with Sedum continued to sustain cover and biomass whereas planted forbs and grasses mostly disappeared, and these treatments were overtaken by Sedum. Our findings demonstrate that with irrigation, plantings of forbs and grasses can sustain plant cover and biomass. However, Sedum buffers against major changes to environmental conditions or abrupt changes to maintenance, adding insurance against failure of extensive green roofs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".