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Record W4377042876 · doi:10.2166/wpt.2023.079

Impacts of climate and land use/cover change on mini-hydropower generation in River Kyambura watershed in South Western part of Uganda

2023· article· en· W4377042876 on OpenAlexfundno aff
Musa Aruho Tusingwiire, Martin Dahlin Tumutungire, Jotham Ivan Sempewo, Swaib Semiyaga

Bibliographic record

VenueWater Practice & Technology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersInternational Development Research CentreUniversity of Dar es Salaam
KeywordsDownscalingWatershedHydropowerEnvironmental scienceSoil and Water Assessment ToolLand useHydrology (agriculture)Climate changeLand coverSWAT modelWater resource managementLand use, land-use change and forestryVegetation (pathology)Watershed managementPrecipitationStreamflowGeographyDrainage basinMeteorology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.261
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

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