Evaluation of the runoff potential in high relief semi-arid regions using remote sensing data: application to Bolivia
Bibliographic record
Abstract
The objective of this study is to develop a methodology based on the combined use of optical and radar images for evaluating the runoff potential in high relief, semi-arid environments. We present a method based on the use of multisource satellite images (RADARSAT-1 SAR, Landsat-7 ETM+ and SPOT-4 HRV) for determining land use and the vegetation cover ratio in order to evaluate the runoff potential in Bolivia. Various band combinations provide classification rates greater than 80% for the mapping of land use. Basically, in over 83% of the watershed, the runoff potential is greater than 0.5 on a scale from 0 to 1. Résumé.La contribution fondamentale de l'étude est le développement d'une méthodologie permettant l'utilisation conjointe d'images optique et radar dans l'évaluation du potentiel de ruissellement en milieu semi-aride de forte énergie de relief. Nous avons conçu une méthode d'exploitation d'images satellitaires multisources (RSO de RADARSAT-1, ETM+ de Landsat-7 et HRV de SPOT-4) afin d'évaluer le potentiel de ruissellement en Bolivie. Plusieurs combinaisons de bandes donnent des taux de classification supérieurs à 80% pour la cartographie de l'occupation du sol. Sur 83% du bassin, le potentiel de ruissellement est supérieur à 0.5 sur un maximum de 1.
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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 | 0.001 | 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) | 0.000 | 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".