Water erosion risk mapping using derived parameters from digital elevation model and remotely sensed data
Bibliographic record
Abstract
The aim of this study is to map the areas exposed to water erosion risks in the High Atlas Mountains of Morocco around the Hassan-I dam. The methodology is based on the analysis of the water power index (WPI) as a hydrological parameter, the vegetation cover, and the litho-logical units. The WPI was derived from a Digital Elevation Model (DEM) and the litho-logical units and vegetation cover were derived from Advanced Land Imager sensor on the Earth Observing-1 satellite platform. The image was corrected from radiometric and atmospheric effects, and geometrically rectified using a DEM and grounds control points. These variables were integrated in a Geographical Information Systems environment, and Multi-Criteria Analyses were used to derive the water erosion risks map pointing out the most exposed areas requiring the implementation of suitable conservation measures. The validation of the obtained results shows the simplicity and the potential of this approach for water erosion risks mapping.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".