Optimizing the Management of Soil Erosion
Bibliographic record
Abstract
Abstract To reduce the environmental and economical impact of soil erosion resulting from improper management of land-use activities, a study was initiated by the Iranian Ministry of Construction on Syahrood, one of the sub-basins of the Damavand watershed in Iran. Land-use optimization is one of the appropriate strategies for soil conservation. It can empower the decision maker or watershed manager to choose from different land-use scenarios to reach the best decision with the different combinations of variables. The output results of the sediment yield model, including the integration of the Modified Universal Soil Loss Equation (MUSLE) with Spatial Analysis System-Geographic Information System (SPANS-GIS), along with the net income of each land use were used as input in the land-use optimization model for minimizing the sediment yield and maximizing farm production of each land use. The multi-objective linear programming simplex method of Steuer (1995) was used to solve the problem. The optimization process allocated dryland farming areas to rangelands if no changes were made to the current supporting practice system. The expected annual sediment yield from the entire sub-basin was reduced by 2420 tonnes/year (or by 5%) and the annual net farm income was increased by 3.99 billion Iranian Rial/year (or by 134%).
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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 | 0.004 | 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 | 0.001 | 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) | 0.003 | 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".