Vegetative filter strips’ performance on sediment erosion and deposition in agricultural drainage ditches of the littoral zone of Eastern Canada
Bibliographic record
Abstract
The littoral zone of Lake Saint-Pierre in Québec, Canada, faces significant sediment deposition in agricultural drainage ditches due to snowmelt floods and human activities in uplands, negatively impacting local fish habitats. This study evaluates the effectiveness of vegetative filter strips (VFS) in mitigating sediment erosion and deposition in these agricultural ditches, contributing to best management practices (BMPs) for water quality. Over a period from November 2019 to June 2023, VFS of various widths (0 m as control, 2 m, and 4 m) were implemented across three experimental sites to assess their performance. Total station surveys and LiDAR-equipped Unmanned Aerial Vehicles (UAV) were used for data collection and analysis, providing detailed measurements of sediment volumes and changes over time. The results indicated that the 2-m VFS reduced sediment deposition by an average of 17% compared to the control, although one site with a 2-m VFS recorded higher sediment deposits. In contrast, the 4-m VFS significantly reduced sediment deposition by 41%, 34%, and 38% at the three sites over the three-year period. Despite variability due to crop rotations and snowmelt events, VFS demonstrated a notable ability to prevent sediment deposition. The study identified several challenges in VFS implementation, including maintenance issues and the growth of in-ditch vegetation affecting efficiency. Furthermore, the comparison between LiDAR and total station surveys showed strong accuracy and consistency for LiDAR, though site-specific factors affected performance. This study underscores the significant role of VFS in sediment control and highlights the need for continuous research and monitoring to overcome practical challenges and enhance BMPs for sustainable agriculture in the littoral zones. The findings suggest that while VFS can effectively reduce sediment deposition, their performance is influenced by site-specific conditions and seasonal variations
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".