Impacts of climate and land use/cover change on mini-hydropower generation in River Kyambura watershed in South Western part of Uganda
Bibliographic record
Abstract
Abstract This study explored the combined impacts of climate and land-use change on mini-hydropower generation in the Kyambura watershed. The soil and water assessment tool (SWAT) was used as a hydrological model whereas the statistical downscaling model (SDSM) was used to downscale meteorological data for the Kyambura watershed for the year 2050. The results show that there will be an increase in urban land by 11.89%, barren land by 25.78%, water by 0.49% and a reduction in percentage area coverage of vegetation by 38.17% by the year 2050. A 10.6 and 17.7% increase is anticipated in average annual hydropower generated by the year 2050. There is, therefore, a need to develop governing policies to regulate management practices to preserve the integrity of the watersheds and ensure the reliability of power production.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".