Characterization of Chyulu Hills Watershed Ecosystem Services in South-Eastern Kenya
Bibliographic record
Abstract
Water is an important good provided by watersheds and is critical in sustaining life and socio-economic development. This study evaluated water provision in the Chyulu Hills watershed which is one of the important dry land water catchments in Kenya. Surface water discharge was mainly through rivers and springs with the latter being more prevalent than the former while ground water provision was primarily through boreholes and shallow wells. Springs discharged the highest water volume annually estimated at 128.14million m3 or 85.14% of all the water discharged in the watershed, with shallow wells and boreholes discharging the least water. For all the springs found in the watershed, Mzima springs discharged the highest water volume estimated at nearly 113.15 million m3 annually or 88.30% of the water produced by all the springs; and about 75.19% of the water in the entire watershed. Rivers which were active had a total water discharge of about 20.66million m3 per year, with Kibwezi River discharging 17.59 million m3 annually or 85.14% of all the water from rivers. Shallow wells were mainly prevalent at Nthongoni and the Makindu, and their annual water discharge was about 16.96million m3 per year with boreholes contributing 98.87% (approximately 16.76 million m3/yr.) of the ground water, and the rest (19,169.92m3/yr.) being contributed by shallow wells. The total amount of water discharged in the watershed by the combined water source types was about 150.49 million m3/yr.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".