Toxicity of sediments in eight urban stormwater management ponds: bioassessment by oligochaete community metrics used in the sediment quality triad
Bibliographic record
Abstract
Implemented for decades as part of the 'best management practices (BMPs)' for controlling urban runoff impacts on receiving waters, stormwater management ponds (SMPs) have been increasingly viewed as potential habitats for urban wildlife. However, since SMPs are subject to a lot of environmental constraints, research toward assessing their ecological quality and their actual benefits as habitats for biota is needed. In this study, the sediment toxicity of eight SMPs located in Southern Ontario, Canada was assessed using the sediment quality triad (SQT) approach. Sediment samples were collected for chemical, ecotoxicological and biological analyses. An oligochaete-based index approach (Oligochaete Index of Lake Bioindication and percentage of pollution-sensitive species) was used as the biological endpoint and integrated into a weight-of-evidence approach to assessing the general sediment quality of the ponds. Our results showed that (i) heavy metals in the sediment and (ii) chloride concentrations in the sediment interstitial water caused detrimental effects on the ecological quality of the sediments in the ponds studied. The oligochaete indices applied in this study showed value as biological endpoints to be integrated into the SQT and used for setting up sediment ecological quality goals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".