Seeking balance: a climate-responsive water management system for Southern Manitoba’s provincial waterways with Shannon Creek as an exemplar
Bibliographic record
Abstract
This practicum answers the research question, “how to create a climate-responsive water management system within a working agricultural landscape.” Through literature review of the natural and human history, GIS mapping, careful site analysis, and extensive site visits, the landscape’s inherent way of being began to emerge as a path forward for climate resilient provincial waterways. Policy, land use, and crop inventory analysis make the current state clear and existing watershed district / landowner partnerships offer a platform for broader scale interventions. This practicum proposes to use Manitoba’s GROW program as a mechanism to incentivize farmers to adopt the intervention. Provincial waterways were once natural creeks and rivers that have been channelized to assist spring runoff from agricultural fields. These ecologically poor waterways are dozens, if not hundreds, of kilometres long and are maintained by a variety of groups: the province, the rural municipality, or the adjacent landowner. Therefore, an intervention in a waterway must be easy enough to be adopted by this variety of groups but also means the capacity for having a substantial impact on water across the province is markedly increased when compared with a small, single site, intervention. Knowledge gained through background research and on-the-ground site analysis led to a relatively simple intervention that employs three design elements already found in the region: shelterbelts, wetlands, and prairie grasslands. These three elements combine to capture snow and eventual spring melt, create habitat, clean agricultural pollutants from the waterway, and hold water in place, ultimately increasing the water storage capacity of provincial waterways by a significant 70%. This practicum leans on vernacular practice, embraces the inherent extremes of the prairie ecoregion, and offers a way to maintain a productive agricultural landscape while responding to the increasing challenges of the climate crisis.
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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 | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".