Effect of vegetative filter strip on sediment deposition in drainage ditches in littoral zone of Lake Sainte-Pierre in Quebec, Canada
Bibliographic record
Abstract
Abstract. Agricultural drainage ditches tend to be clogged due to the sedimentation in the coastal area of Lake Sainte-Pierre in Quebec, Canada, where water flows at a low velocity and snowmelt-originated seasonal inundation occurs. Frequent dredging is required to ensure drainage performance, posing a significant economic burden on agricultural producers. This research aims to discover the possibility of using vegetative filter strips to reduce the ditch sedimentation rate and evaluate Unmanned Aerial Vehicle (UAV)-based LiDAR‘s ditch sedimentation monitoring ability in the densely vegetated drainage ditch condition. These sites were established in the coastal area of Lake Sainte Pierre near St. Cuthbert, Yamachiche and Baie-du-Febvre. Starting from Nov. 2019, three ditches in all three sites are treated with 0, 2-m, and 4-m vegetative filter strips. Four rounds of cross-section measurements were conducted at a 20-m interval of all ditches using an electronic total station in 2019 â 2021 to calculate sediment accumulation in drainage ditches. In addition to the total station survey, the ditches were scanned using a LiDAR-equipped Unmanned Aerial Vehicle (UAV) once per year for comparison with the acquired total station data. The results generally suggest no significant difference in sediment deposition during the monitoring period. However, the result showed a reverse effect where most drainage ditches experienced a volume increase across the experiment, indicating soil erosion, not sedimentation, might happen in the Lac Sainte-Pierre‘s drainage ditch. Also, a decimeter level of difference has been found between the LiDAR data and the total station data showing the limited capability of UAV-LiDAR in agricultural drainage ditch sedimentation 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".