Assessing the Impact of Man–Made Ponds on Soil Erosion and Sediment Transport in Limnological Basins
Bibliographic record
Abstract
The impact of ponds on basins has recently started to receive its well-deserved scientific attention. In this study, pond-induced impacts on soil erosion and sediment transport were investigated at the scale of the French Claise basin. In order to determine erosion and sediment transport patterns of the Claise, the Coordination of Information on the Environment (CORINE) erosion and Soil and Water Assessment Tool (SWAT) models were used. The impact of ponds on the studied processes was revealed by means of land cover change scenarios, using ponded versus pondless inputs. Results show that under current conditions (pond presence), 12.48% of the basin corresponds to no-erosion risk zones (attributed to the dense pond network), while 65.66% corresponds to low-erosion risk, 21.68% to moderate-erosion risk, and only 0.18% to high-erosion risk zones. The SWAT model revealed that ponded sub-basins correspond to low sediment yields areas, in contrast to the pondless sub-basins, which yield appreciably higher erosion rates. Under the alternative pondless scenario, erosion risks shifted to 1.12%, 0.52%, 76.8%, and 21.56% for no, low, moderate, and high-erosion risks, respectively, while the sediment transport pattern completely shifted to higher sediment yield zones. This approach solidifies ponds as powerful human-induced modifications to hydro/sedimentary processes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".