Spatial and seasonal toxicity in a stormwater management\nfacility: Evidence obtained by adapting an integrated\nsediment quality assessment approach
Bibliographic record
Abstract
Stormwater ponds have been widely used to control increased surface runoff resulting from urbanization, and to enhance runoff quality. As receiving waters, they are impacted by intermittent stormwater pollution while also serving as newly created aquatic habitats, which partly offset changes of aquatic ecosystems and their biodiversity by urbanization. Thus, determining ecological risks in stormwater ponds is important for the preservation\nand rehabilitation of biodiversity in urban areas. Limitations of the conventional toxicity assessment techniques in stormwater ponds have led us to use the sediment quality triad approach with the specific analyses of oligochaetes. The latter analyses build on the earlier work by the Cemagref (Lyon, France) and use the oligochaetes as bioindicators of the sediment quality. This integrative approach was tested at eight sites in the TerravieweWillowfield stormwater facility in Toronto, Ontario, in all four seasons (summer 2008espring 2009). The facility receives direct runoff from the MacDonald-Cartier freeway with a traffic intensity of 340,000 vehicles/d. Sediment chemistry results indicate that\nseveral heavy metals and PAH compounds exceeded the Ontario sediment quality guidelines in the facility. Regardless of the season, laboratory bioassays revealed a strong spatial\nvariation in sediment toxicity along the flow path from the inlet to the outlet, agreeing with decreasing concentrations of contaminants in sediment, especially of heavy metals.\nHowever, in situ assessments of the benthic macroinvertebrate community structure and in particular of the oligochaete community revealed an overriding influence of\nseasonally varying toxicity. This seasonal pattern was described as high toxicity in spring and recovery in fall and corresponded to the influx and flushing-out of road salts and of several heavy metals within the facility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".