Stockyards wetland park: filtering the Mission Creek watershed
Bibliographic record
Abstract
In a time of rapidly escalating climate change and increasing urbanization, cities are becoming a microcosm of climate change effects that require a response through the built environment to mitigate these issues. In Winnipeg, this will mean more intense storm events year-round and a variation between droughts and floods yearly, among other problems such as prolonged heat waves in the summer months. With more precipitation predicted for Winnipeg and the continued use of the combined sewer system causing sewage overflows into Winnipeg’s rivers, there is a need to look to green infrastructure to assist or replace Winnipeg’s grey infrastructure for water management. Green infrastructure in the form of constructed wetlands can be strategically incorporated along many of Winnipeg’s creeks to help manage higher volumes of water and purify it before it is released into the rivers. Constructed wetlands can help clean the Red, Assiniboine, and Seine Rivers, positively affecting the endangered Lake Winnipeg downstream, and double as park spaces to add to Winnipeg’s park system. This practicum investigates sites around Winnipeg that provide the potential for implementing constructed wetlands. It also contains a site design concept to show the possibilities of constructed wetlands and how they could be used in Winnipeg to improve the city’s water management system through green infrastructure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".