Using remote sensing to assess how intensive agriculture impacts the turbidity of a fluvial lake floodplain
Bibliographic record
Abstract
Lake St-Pierre is set in the largest floodplain in the province of Quebec, Canada, and is a rich ecosystem of great ecological importance. However, Lake St-Pierre has seen its ecological integrity deteriorate in recent decades, largely due to the development of agriculture in and around its floodplain. This study uses a simple turbidity retrieval model (NIR and RED reflectances from Sentinel-2) to quantify the impact of land use on water turbidity within the lake floodplain during the 2019 and 2020 spring flood. Using a linear mixed effect model, we assessed how land use (wet meadows, cultivated grasslands, soybean, corn fields) impacts turbidity retrieved from Sentinel-2. Water turbidity was found to increase with the level of agricultural perturbations. During the severe and long 2019 flood, the turbidity was 5% higher over cultivated grassland fields, 35% higher over soybean fields and 70% higher over corn fields, compared to wet meadows. For 2020, a comparatively drier year, the corresponding impacts were 9% for cultivated grassland fields, 65% for soybean fields and 93% for corn fields. However, land use only explained 4% (2019) and 5% (2020) of the water turbidity spatiotemporal variance, which was instead mostly driven by the sediment load coming from upstream watersheds. Our results indicate that, despite complex processes driving water mass movements in the floodplain, intensive farming practices lead to higher water turbidity compared to natural lands. This study provides evidence that intensive agriculture impacts the water quality in a critical moment for the long-term health of the Lake St-Pierre.
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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 | 0.000 |
| 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".