Assessing the effects of land use on water turbidity in a fluvial lake floodplain using Sentinel-2 imagery
Bibliographic record
Abstract
ABSTRACT Lake Saint-Pierre (LSP), the largest fluvial lake in the St-Lawrence River system, is a vital freshwater floodplain with rich biodiversity. However, expanding agricultural activities have disrupted the landscape, increasing turbidity and threatening the ecosystem. In response, the Quebec government launched the LSP strategic research cluster in 2018 to encourage sustainable land and water management. This study examines how different land-use types affected turbidity during the spring floods of 2019, 2020, and 2022, using Sentinel-2 satellite imagery. Land types included conventional and improved agriculture, cultivated and natural grasslands, and flooded forests. A new empirical model for flooded forests showed strong accuracy (adjusted R2 of 0.88, RMSE (root mean square error) of 10.91 FNU (Formazin Nephelometric Unit)). Combined with an existing model for open water, turbidity maps were created across all land types. Using a linear mixed model, we found that conventional and improved practices increased turbidity in the LSP floodplain by up to 600% compared with natural forests. Grasslands also contributed to higher turbidity, though to a lesser extent. The findings underscore that even improved practices cannot fully mitigate turbidity. Effective control requires integrated watershed management, including upstream inputs and targeted best practices to protect the LSP floodplain.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".