Modelling soil erosion and sediment transport under different land management options in a southern-italy watershed
Bibliographic record
Abstract
The aim of this study is to investigate the infl uence of different land management options on the sediment \nload at the watershed scale. To reach this, the Annualized agricultural non-point source model \nwas used in the Candelaro basin (2300 km2). The watershed is located in a semi-arid area of southern \nItaly (Puglia region) and is affected by extensive erosion processes on the hillslopes. The sediment \ntransport simulations have been compared with the 15 years (1970–1984) data coming from measures \ntaken in two sub-watersheds (Vulgano and Salsola). Later, the model has been applied for a period of \n24 years (1985–2008) to evaluate the effects of different land management options on the sediment \nyield: traditional best management practices, environmentally targeted agricultural practices and water \nand soil conservation works. The results obtained in the fi rst part of the work show that the Annualized \nagricultural non-point source model performs well in simulating runoff and sediment yields at the \nwatershed scale. Furthermore, the analysis carried out shows that the model is an effi cient tool to assess \nthe infl uence of different management options in the long term and in different weather conditions. \nKeywords: AnnAGNPS model, sediment yield, soil erosion, surface runoff, watershed management
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".