Hydro-climatic trends and water resource management implications based on multi-scale data for the Lake Victoria region, Kenya
Bibliographic record
Abstract
Unreliable rainfall may be a main cause of poverty in rural areas, such as the Kisumu district by Lake Victoria in Kenya. Climate change may further increase the negative effects of rainfall uncertainty. These effects could be mitigated to some extent through improved and adaptive water resource management and planning, which relies on our interpretations and projections of the coupled hydro-climatic system behaviour and its development trends. In order to identify and quantify the main differences and consistencies among such hydro-climatic assessments, this study investigates trends and exemplifies their use for important water management decisions for the Lake Victoria drainage basin (LVDB), based on local scale data for the Orongo village in the Kisumu district, and regional scale data for the whole LVDB. Results show low correlation between locally and regionally observed hydro-climatic trends, and large differences, which in turn affects assessments of important water resource management parameters. However, both data scales converge in indicating that observed local and regional hydrological discharge trends are primarily driven by local and regional water use and land use changes.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".