Stormwater ponds can contain comparable biodiversity to unmanaged wetlands in urban areas
Bibliographic record
Abstract
Urban freshwaters provide a range of ecosystem services, including stormwater management, water treatment, biodiversity, and aesthetics. Management of freshwaters should aim to maximise as many of these services as possible, but managers are often focused on individual services. To test for the biodiversity value of stormwater management ponds (SMPs) in Ottawa, Canada, 20 SMPs were surveyed for macroinvertebrates using standardised sampling techniques. These were compared against 10 wetlands that were not managed for stormwater control (a combination of ornamental lakes, natural lakes, and nature reserves) in and around the same urban area (a total of 30 ponds). Natural wetlands and SMPs were very different in their water chemistry, which was correlated with the proportion of urban land use within 1 km of the site, with higher conductivity in SMPs with increasing urban land cover (P = 0.046). Despite this, natural wetlands and the richest SMPs contained similar levels of biodiversity and similar macroinvertebrate community structure. This study highlights that stormwater management can occur alongside biodiversity enhancement in urban areas, but correlations between urban land use, water chemistry, and the structure of biological communities suggests that run-off from urban areas is likely a major factor in structuring biological communities in built-up regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".