Stormwater Management Ponds vs Natural Wetlands: A Question of Avian Biodiversity
Bibliographic record
Abstract
As the global urban population grows, natural features, such as wetlands are being lost to urban development.In their place, Stormwater Management ponds are being built to mitigate the effect of wetland loss on hydrological function of the environment.These engineered ponds are not built to replace the loss of wildlife habitat and it is unclear if they support the same biodiversity as natural ponds.We studied 30 Stormwater Management ponds and 31 natural ponds to estimate the difference in the number of wetland bird species occupying each pond type.We predicted that Stormwater Management Ponds would not be able to support the same level of biodiversity and would have fewer species occupying them due in part to the surrounding landscape changes and vegetation heterogeneity.We tested our predictions using a Bayesian hierarchical occupancy model to account for imperfect detection.Our main prediction was correct that Stormwater Management Ponds do not support the same number of wetland bird species.Additionally, outside of pond type both landscape composition and vegetation heterogeneity had little affect on species occupancy.Understanding the role these ponds play in supporting wetland birds is important because we are continuing to lose wetland habitat and we need to understand their role in supporting urban biodiversity.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".