Sediment Assessment of Stormwater Retention Ponds within the Urban Environment of Calgary, Canada
Bibliographic record
Abstract
Abstract The treatment of urban stormwater by retention ponds is known to be effective for water quality improvement as well as storm flow management and, in the past two decades, has become widely implemented. However, limited research has been conducted on the quality of sediment deposited in ponds. Therefore this study focuses on contaminant concentrations within the sediment from stormwater ponds built in Calgary, Canada. Electrical conductivity and the sodium adsorption ratio consistently exceeded the Canadian Council of Ministers of the Environment (CCME) agricultural soil quality guidelines, indicating a city-wide salt contamination issue. F3 hydrocarbon fractions, cadmium, chromium, copper, lead, selenium, and zinc were also identified as parameters of concern. In particular, the 61 Avenue SE duck pond displayed the greatest diversity and severity of contaminants due to the industrial catchment area. Removal and disposal options were limited due to the characteristics of the sediment. The examination of the solids content illustrated that all retention ponds will require the sediment to be dewatered prior to disposal. Disposal options were subsequently restricted to landfill disposal due to salt, metal, and/or hydrocarbon parameters exceeding CCME soil guidelines. One exception was the Deerfoot Trail and Highway 22X pond which could be directly disposed of in areas designated as commercial and industrial land use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".