Terraview and Willowfield Stormwater Pond Sediment and Water Quality
Bibliographic record
Abstract
Stormwater ponds have been widely used to control increased volumes and rates of surface runoff resulting from urbanization. Stormwater ponds have also been designed to provide multiple other benefits; including protection of downstream waters, sediment and habitat (land/aquatic) quality, provision of educational, recreational use as well as aesthetic amenities. Stormwater ponds create unique opportunities for enhancing community benefits, but they also cause ecological concerns with respect to the quality of the newly created habitat. Stormwater ponds receive untreated runoff from urban areas and transportation corridors, and such runoff transports sediment and pollutants from urban sources into the stormwater facilities. Built in the mid 1990s the Terraview and Willowfield stormwater ponds currently receive water and sediment runoff from 9 hectares of a 16-lane freeway and runoff from 30 hectares of residential lands. This dataset contains stormwater data from two stormwater ponds (Terraview and Willowfield) located within the Toronto and Region Area of concern (AOC). The data are water and sediment chemistry, and are used to evaluate the effectiveness of the two-stormwater ponds in providing a suitable habitat for aquatic species and wildlife species over a two-year period (2007 and 2008). Water and sediment samples were analyzed for trace metals such as calcium, nickel, and lead as well as 16 priority polycyclic aromatic hydrocarbons (PAHs).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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".