Algal Communities as a Biological Indicator of Stormwater Management Pond Performance and Function
Bibliographic record
Abstract
Abstract An investigation into phytoplankton and periphyton algal communities of two recently constructed Stormwater management ponds suggests that Stormwater impacts on biological communities are reduced during passage through the ponds, providing a degree of protection for biological communities in their receiving waters. In both ponds, disturbance effects from the incoming Stormwater on algal community richness and evenness appear to be greatest in the sediment forebay and are reduced in the main pond. However, the nature of the disturbance in the two systems can be seen to be fundamentally different from a biological perspective, with Rouge Pond functioning primarily to reduce toxins harmful to algal communities (e.g., heavy metals), and Harding Pond acting to reduce nutrients. The taxonomic composition of the two sites provides an indication of the quality of the incoming Stormwater. Rouge Pond, which contains many marine and brackish water species, receives Stormwater runoff from a major highway, while Harding Pond, containing more nutrient rich species, receives Stormwater primarily from residential properties. Despite the nutrient-rich conditions present in both ponds, nuisance blue-green algae (cyanobacte-ria) are conspicuously absent, and the ponds appear to have little potential for developing harmful algal blooms. The lack of blue-green algae can be linked to the hydraulic functioning of the ponds, suggesting that Stormwater facilities may be engineered to inhibit undesirable algal communities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".