Landscape controls on seston stoichiometry in urban stormwater management ponds
Bibliographic record
Abstract
1. Stormwater management ponds (SWMPs) are taking the place of natural ponds and wetlands in urban areas. SWMPs have the potential to serve as hotspots for nutrient cycling, yet little is known about how urban catchments affect nutrient chemistry and stoichiometry within these ponds. 2. We sampled 50 SWMPs in Southern Ontario, Canada, to characterise their seston stoichiometry and make comparisons with published lake and pond data and models of seston stoichiometry. We tested (i) whether C : N : P ratios were similar to natural ponds and small lakes, (ii) whether seston stoichiometry was scale dependent and (iii) whether variability in seston chemistry could be explained by landscape and pond characteristics, such as catchment imperviousness and hydrological condition (based on recently received rainfall). 3. Seston C : N and C : P ratios were significantly lower in SWMPs than published ratios for small lakes, likely because of high nitrogen and phosphorus concentrations in SWMPs. Our results also showed no dependency of stoichiometric ratios on pond size. Analyses of ratios versus landscape and pond characteristics revealed significant relationships only when ponds were grouped based on the hydrological condition of the catchments. 4. It is likely that SWMPs function very differently during wet and dry periods. When SWMPs are disconnected from the landscape after a lengthy dry period, internal processes become increasingly important for seston stoichiometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; both teacher heads agree on what is shown here.
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".