EMBRACE THE FLOOD: Introducing a Symbiotic Lifestyle: Designing Resilient and Livable Landscapes in Winnipeg by Integrating Nature-Based Solutions into the Urban Water System
Bibliographic record
Abstract
The city Winnipeg, built on the wet prairie, has been threatened by severe flooding for decades. The initial decision to build the city at the confluence of the Red and Assiniboine rivers led to the inevitable consequence of recurrent natural flooding. The floods prompted the provincial government to take a series of flood control structural measures to prevent the city property. However, the waterlogging crisis has still not subsided. During the significant territorial transformations in the last two centuries, the rivers and creeks have been heavily dammed and channelized. The hasty attempt at city development resulted in immediate economic benefits, but at the expense of eliminating the land’s ability to drain water through naturally occurring streams and coulees. Due to environmental impacts on a wider geographical scale, most notably climate change, both floods and droughts are hitting the extremes, which is escalating the challenge on flood management. One of the main obstacles in this project is to solve the problem of threatening floodwaters and turn the negative impacts into the opportunities for a more sustainable lifestyle. Tracing back to last century, the indigenous peoples of the plains used to share a mutually beneficial living pattern with beavers and other species on the prairies. This coexistence approach towards humans, wildlife and nature deserves an opportunity to be brought back to Winnipeg. Through flood-mitigation measures based on natural processes, the urban hydrologic system is anticipated to move toward sustainable development and provide fairness and reliability to the living environment and urban ecosystems in response to challenges posed by habitat loss, climate change, and emerging conservation issues.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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 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".