Agricultural activities lead to sediment infilling of wetlandscapes in the Canadian Prairies: Assessment of contributions by tillage, water and wind erosion
Why this work is in the frame
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Bibliographic record
Abstract
Soil erosion and sediment delivery models have been increasingly employed in studies of catchment sediment dynamics in current years. These models are also used to represent the spatial interaction of soil erosion and sediment transport processes, thereby providing spatially-distributed predictions of soil redistribution rates for agricultural landscapes. It has been widely recognized that individual soil erosion processes and their interactions contribute towards total soil erosion; however, quantifying the rates and patterns of soil erosion processes and their interactions within topographically complex landscapes is challenging. Therefore, the objective of this research was to estimate and model the relative contributions of tillage, water and wind erosion towards total soil erosion in the Canadian Prairie provinces of Manitoba and Alberta using 137Cs, a fallout radionuclide tracer, and three well-established models: (i) Tillage Erosion Model (TillEM); (ii) Revised Universal Soil Loss Equation, version 2 (RUSLE2); and (iii) Single-event Wind Erosion Evaluation Program (SWEEP). The findings indicate that the patterns of 137Cs-estimated soil erosion were closely matched with the erosion pattern predicted by TillEM and suggests that tillage erosion dominates the pattern of total soil erosion on the knolls of hummocky landscapes. Additionally, soil particle size variation within the wetland catchments reflected the modeled patterns of water and tillage erosion. Furthermore, our findings confirmed that colour coefficients are useful in identifying spatial heterogeneity of soil within wetland catchments and reflect the patterns of soil loss and gains. These results indicate that water and tillage erosion, and also their interactions, are main erosion processes in the Canadian Prairie Pothole Region (PPR), but that soil movement by tillage practices has been the predominant redistribution process.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it