Soil degradation mobilizes soil nutrients placing Canadian Prairie wetlands at risk
Bibliographic record
Abstract
Abstract Depressional wetlands in the Canadian Prairies are experiencing degradation due to intensive agricultural practices in adjacent upland areas. Depressional wetlands within cropland are particularly affected, as increased sedimentation and runoff elevate nutrient levels within these ecosystems. This study assessed soil organic carbon (SOC) and soil particulate phosphorus (SPP) stocks, fluxes, and balances within the contributing catchment area to depressional wetlands. Catchment‐scale sediment tracing using 137 Cs and budgeting methods was employed to examine the interactions between soil degradation, sediment movement, and nutrient redistribution. Analysis of 165 soil/sediment cores from eight wetland catchments revealed spatial heterogeneity in SOC and SPP stocks across upper‐, middle‐, and lower‐slope and depression topographical sequences. SOC levels ranged from 6.9 to 104.1 kg m −2 , while SPP varied from 0.06 to 1.9 kg m −2 within soil depth profile. Riparian areas at slope bottoms emerged as key accumulation sites for sediment, SOC, and SPP, underscoring their role as natural filters that intercept sediment and nutrients before they reach wetlands. These findings highlight the vulnerability of Canadian Prairie depressional wetlands to sediment and nutrient loading, emphasizing the need for soil erosion control strategies—such as the conservation of riparian buffers—to mitigate the adverse effects of agricultural activities on these vital ecosystems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".