Rethinking water drainage in a prairie landscape: Seine River Diversion
Bibliographic record
Abstract
This practicum focuses on improving the ecological conditions of the Seine River Diversion located near the city of Winnipeg. The Diversion is one of Manitoba’s many engineered drainage infrastructures with poor water quality, lacking in biodiversity and habitat. The main objectives of the practicum are improving the water quality in the Diversion, habitat and biodiversity restoration, and providing recreational opportunities to create a new public space adjacent to the Diversion. After conducting a detailed investigation of the existing conditions, historical context and the Diversion’s impact on the large-scale landscape that emerged from under the glaciers, sites parallel to the Diversion that possess a potential for meeting the practicum’s objectives are identified. One site is distilled from the many potential sites, and a prototype design proposal that meets the goals and objectives of the practicum is developed in detail. An examination of the projects that can act as precedents for the prototype design is carried out to determine a sustainable and resilient design strategy. The prototype design utilizes green infrastructure to propose a self-sustainable system that satisfies the practicum’s objectives. Wetlands are proposed to collect, retain and treat the impure water from many sources of pollution before its release into the Diversion. The proposal improves the ecological conditions of the Seine River Diversion and tackles the issues of loss of habitats and biodiversity that are present globally.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".