Hydrological Balance in Moose Mountain Provincial Park
Bibliographic record
Abstract
Closed-basin lakes in Canada’s semi-arid prairie region experience significant water level fluctuations due to climate change and hydrological variability. In Moose Mountain Provincial Park, Saskatchewan, the increasing beaver population is further influencing the water levels of Kenosee Lake, the park’s largest and most utilized water body. Beavers (Castor canadensis), known as ecosystem engineers, modify landscapes through dam-building, creating wetlands that alter surface water dynamics. This study investigates the relationship between lake levels, beaver activity, and climate change by simulating the water balance of Kenosee Lake. The Cold Region Hydrological Model (CRHM) was used to simulate the lake’s hydrological regime, while the Watershed Modeling System (WMS) and Wetland DEM Ponding Model (WDPM) were used to understand hydrological flow paths in the Kenosee Lake’s watershed. Model performance was evaluated by comparing CRHM simulations with observed water levels from 2002 to 2013 and 2020 to 2024, demonstrating agreement between simulated and real-time data. Future projections extending to 2100 indicate that climate change will lead to increased hydrological variability, with more extreme fluctuations in water levels. Additionally, beaver activity is expected to further alter water storage and distribution, potentially exacerbating or mitigating climate-induced changes depending on hydrological conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".