The Green Roof Microbiome: Improving Plant Survival for Ecosystem Service Delivery
Bibliographic record
Abstract
Plants are key contributors to ecosystem services delivered by green roofs in cities including stormwater capture, temperature regulation, and wildlife habitat As a result, current research has primarily focused on their growth in relationship to extensive green roof (e.g. substrates <15cm depth) ecosystem services. Green roofs are exposed to a variety of harsh abiotic factors such as intense solar radiation, wind, and isolation from ground-level habitats, making survival exceedingly difficult. Plants in natural habitats benefit from a variety of interactions with fungi and bacteria. These plant-microbial interactions improve mechanisms of survival and productivity; however, many green roof substrates are sterilized prior to installation and lack microbial communities with unstudied consequences for green roof plant health and subsequent survival and performance. In this paper, we present six hypotheses on the positive role of microbes in green roof applications. In natural and experimental systems, microbial interactions have been linked to plant (1) drought tolerance, (2) pathogen protection, (3) nutrient availability, (4) salt tolerance, (5) phytohormone production, and (6) substrate stabilization, all of which are desirable properties of green roof ecosystems. As few studies exist that directly examine these relationships on green roofs, we explore the existing ecological literature on these topics to unravel the mechanisms that could support more complex green roof ecosystem and lead to new insight into the design, performance, and broader applications in green infrastructure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".