Re-naturalizing the Norquay Channel: a strategy to improve water quality
Bibliographic record
Abstract
Southern Manitoba used to contain multiple marsh and bog wetlands. The wetlands were drained during the early 20th Century to increase the amount of arable land and attract more people to the province. These wetlands provided critical ecological functions such as flood mitigation, water filtration, and increased biodiversity. Since artificial waterways drained the wetlands, the surface water quality of the receiving waterbodies has suffered, with Lake Winnipeg taking the brunt of it. Two research questions drive this practicum: how can we, as landscape architects, integrate ecologically functioning wetlands into major tributaries of the Red and Assiniboine Rivers to improve their water quality? Given its physio-geographical specifics, what form might these take within the Red River Valley? In response to the prior questions, a constructed wetland design was proposed along a portion of the Norquay Channel. The agricultural landscape of the Red River Valley creates different various constraints for the design. The design includes important characteristics of natural wetlands, such as gentle slopes to facilitate the growth of wetland vegetation and a sediment deposition pond, all while working within a compact space to minimize the amount of agricultural land affected. Since the impact of the individual design site on improving Lake Winnipeg’s water quality would be limited, a more significant regional intervention is needed and proposed. Other fourth and fifth-order waterways, similar to that of the Norquay Channel, are proposed for similar interventions. Complete data from 2022 for surface water quality and water flow are used to estimate the impact of the individual design site on the nutrient loading of subsequent waterways. Two leading nutrients, nitrogen and phosphorus, could potentially be reduced within the wetlands by 46 to 56% and 70 to 89%, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".