Decision Support Tool for Evaluating Changes in Arid and Tropical Watersheds
Bibliographic record
Abstract
During the last three decades, many arid and semi-arid watersheds have been affected by major hydrological changes because of human interventions such as deforestation and other agricultural changes, as well as climate fluctuations and/or changes. The primary objective of this study was to investigate whether recent changes are the result of climatologic variability or anthropologically induced transformations over the past years. A secondary objective was to provide a more practical approach to assess actual changes in the hydrological response of a watershed in an arid and tropical region. The methodology used in this study involved combining remotely sensed image data from satellites with in-situ hydrological observations from the Minab catchment in the south of Iran. The results of longterm analysis of historical time series on rainfall, land use/land cover, and stream flow were integrated at the landscape level to identify appropriate options for land and water management. It was found that the destruction of natural vegetation resulted in a decrease in the annual total water yield of 20%, with a decrease of 6.5% in the base flow during the low-flow period (May to November), and an increase in the storm runoff during the high-flow period (December to April). While potential evaporation from periods 1 to 3 showed a decrease of 10%, the actual evaporation increased by 9 per cent. It was concluded that climatic variations and land use change are the most important factors affecting the changes in the hydrologic regime of Minab catchment in Iran.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".