Performance of dryland and wetland plant species on extensive green roofs
Bibliographic record
Abstract
BACKGROUND AND AIMS: Green roofs are constructed ecosystems where plants perform valuable services, ameliorating the urban environment through roof temperature reductions and stormwater interception. Plant species differ in functional characteristics that alter ecosystem properties. Plant performance research on extensive green roofs has so far indicated that species adapted to dry conditions perform optimally. However, in moist, humid climates, species typical of wetter soils might have advantages over dryland species. In this study, survival, growth and the performance of thermal and stormwater capture functions of three pairs of dryland and wetland plant species were quantified using an extensive modular green roof system. METHODS: Seedlings of all six species were germinated in a greenhouse and planted into green roof modules with 6 cm of growing medium. There were 34 treatments consisting of each species in monoculture and all combinations of wet- and dryland species in a randomized block design. Performance measures were survival, vegetation cover and roof surface temperature recorded for each module over two growing seasons, water loss (an estimate of evapotranspiration) in 2007, and albedo and water capture in 2008. KEY RESULTS: Over two seasons, dryland plants performed better than wetland plants, and increasing the number of dryland species in mixtures tended to improve functioning, although there was no clear effect of species or habitat group diversity. All species had survival rates >75 % after the first winter; however, dryland species had much greater cover, an important indicator of green roof performance. Sibbaldiopsis tridentata was the top performing species in monoculture, and was included in the best treatments. CONCLUSIONS: Although dryland species outperformed wetland species, planting extensive green roofs with both groups decreased performance only slightly, while increasing diversity and possibly habitat value. This study provides further evidence that plant composition and diversity can influence green roof functions.
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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 | 0.000 | 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) | 0.001 | 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".