The Water Chemistry of Shallow Ponds around Wapusk National Park of Canada, Hudson Bay Lowlands
Bibliographic record
Abstract
Understanding the structure and function of ecosystems in Canada's national parks is essential in fulfilling the Parks Canada Agency's mandate to manage for ecological integrity. Wapusk National Park is representative of the Hudson Bay Lowlands and small lakes and ponds make up a considerable component of the landscape. The Hudson Bay Lowlands have experienced relatively recent isostatic rebound from submarine conditions, hence proximity to the coast was found to be a major factor in determining the concentration of dissolved salts in pond water. It was observed that the ionic composition of the water in ponds throughout much of the park is consistent, indicating that most of the ponds are an expression of surface water maintaining little connection to groundwater. The prevalence of permafrost throughout much of the park is likely the reason for this stability. Ponds within the park also show considerable variability in the amount of dissolved organic carbon (DOC) they contain. DOC shows a strong north-south and east-west trend. Ponds in the northeast of the park have lower DOC values while ponds in the southwest of the park have higher levels of DOC. Changes in DOC within the park appear to be driven by changes in the terrestrial vegetation surrounding the ponds. Climatically mediated changes in northern tree line and permafrost are likely to cause the greatest alteration of aquatic habitats in Wapusk National Park. The paucity of background data makes it impossible to assess the amount of change that may have already occurred in the park. This research provides the first landscape-level study for the area and shows that there are distinct limnological patterns over this landscape that are likely to be sensitive to climate change and should be readily detectable with ongoing monitoring.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".