Are Plant Communities of Roadside Stormwater Ponds Similar to those Found in Natural Wetlands?
Bibliographic record
Abstract
Abstract Stormwater ponds (SP) are increasingly used for water management along roads and in urban environments. How these constructed wetlands compare to natural wetlands in terms of biodiversity remains unclear, however. Studies to date have evaluated the subject in general terms, without considering the different habitats in SP and wetlands (from aquatic, at the pond edge, to terrestrial, at the upper bank) or other local and regional factors. This project aimed to compare the taxonomic diversity and composition of plant communities established in four different habitats of SP with that in either roadside or remote natural wetlands. We also evaluated the effect of various local and regional factors on those communities. Our results show that, globally, the species composition of the lower, wetter habitats was similar between SP and reference wetlands, especially roadside wetlands, while higher, drier habitats showed significant differentiation. Factors explaining observed differences between SP and both roadside and remote natural wetlands were water level fluctuations, road proximity, slope, and age of the SP. Stormwater ponds also exhibited lower beta diversity than both types of wetlands. Nonetheless, our study suggests that with some improvements in design, SP have the potential to more closely resemble natural wetlands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".