Agricultural activities lead to sediment infilling of wetlandscapes in the Canadian Prairies: Assessment of soil erosion and sedimentation fluxes
Bibliographic record
Abstract
Wetlandscapes are vulnerable to land conversion and sediment infilling from upland agriculture, causing them to act as sinks for sediment deposition and putting at risk their ecosystem services. Wetlandscapes in the Canadian Prairies agroecosystems are particularly susceptible to sediment infilling because of the intensification of human activities and agricultural practices. The rising risk of soil erosion in cultivated landscapes has generated a need to estimate soil redistribution rates and soil loss monitoring tools and techniques. This research examines the effects of agricultural activities on soil loss and sedimentation rates within agricultural landscapes in the Canadian Prairies. Land and atmospheric fluxes of sediment into wetlands are quantified over the past 60 years using catchment-scale tracing (137Cs) and budgeting techniques. Findings indicate that the pattern of 137Cs erosion/deposition varies along catchment toposequences, with erosion near the top of the toposequences (the average annual soil erosion rates were found to be 1.1 kg m−2 yr−1 for Manitoba and 0.3 kg m−2 yr−1 for Alberta) and deposition within the wetland ecosystem (total deposition rates were estimated at about −3.6 kg m−2 yr-1for Manitoba and −0.9 kg m−2 yr−1 for Alberta). The sediment delivery ratios were approximately 57% and 35% in Manitoba and Alberta, respectively, indicating that a noticeable amount of the mobilized sediment exits the field. These transfers from cultivated fields into wetlands reveal that wetlandscapes in Canadian Prairies are vulnerable to sediment infilling, and soil erosion control practices are needed to achieve sustainable management of agricultural landscapes.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".