Towards a dynamic effective drainage area map for the Canadian Prairie : sensitivity of contributing area to wetland storage capacity
Bibliographic record
Abstract
Wetlands that occupy topographic depressions are a defining feature of the Canadian Prairie. These features control hydrological connectivity as they contain high storage capacity relative to precipitation and runoff than is typically available. Altering wetland distribution changes the frequency with which areas can become hydrologically connected to the catchment outlet. There are several methods that have proven successful in estimating contributing area response to changes in wetland storage satisfaction or removal, but none have been applied widely across the Canadian Prairie. The objective of this study was to determine if the rate at which contributing area changes with wetland storage capacity satisfaction or removal could be related to wetland distribution, and if so, map sensitivity across the region. To do so, a GIS desktop analysis was employed in which iterative measurements were made of contributing area expansion with simulated wetland storage capacity satisfaction or removal. Results show that those catchments with more small wetlands have more sensitive contributing areas to storage capacity satisfaction or removal. Extrapolation of this relationship across the Canadian Prairie shows that areas in western Manitoba and southeastern Saskatchewan are among the most sensitive. These results provide a better understanding of how contributing area may change with satisfaction of wetland storage deficits or removal of wetland storage capacity and this may benefit agriculture, industry, and efforts to manage floods and nutrient transport to downstream lakes across the region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".