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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".