Characterization of Water Source Types and Uses in Kirisia Forest Watershed, Samburu County, Kenya
Bibliographic record
Abstract
<p class="1Body">Kenya’s dry-land water catchments are valued for their water provision services but their conservation is given little attention. This study was carried out between October and December 2015 and documented water source types and uses by humans and livestock in Kirisia Forest watershed. Different water source types exist in the watershed including earth dams, water pans, shallow wells, boreholes, springs and streams. The estimated population of livestock and locals using these sources was 180,645 and 147, 060 respectively. Earth dams and water pans provided water to the highest population of community members estimated at 11,564 people followed by boreholes at 9,886 people while streams, springs and shallow wells were used by the least number of people. They also provided water to the highest number of livestock estimated at an average of 15,422 animals. The highest amount of water was abstracted from boreholes at nearly 197,720Litres/day (197.72m<sup>3</sup>/day) followed by earth dams and water pans at 91,960Litres/day (91.96m<sup>3</sup>/day), and the least was from shallow wells, springs and streams at about 38,000Litres/day (38m<sup>3</sup>/day). Daily water abstraction from all the water source types by humans and livestock was nearly 366,540Litres/day or 366.54m<sup>3</sup>/day. Twenty three sub-locations rely on water from the watershed and based on the 2009 population census, their projected water demand was approximately 182,238,520Litres/day (182,238.52 m<sup>3</sup>/day). Water demand by livestock was estimated at 12,172,600Litres/day (12,172.60m<sup>3</sup>/day) based on the 2013 population data. Overall water demand by humans and livestock in all the sub-locations was estimated at 194,411,120Litres/day (194,411.12 m<sup>3</sup>/day); and these findings demonstrate the critical role played by the watershed in sustaining locals livelihoods and pastoralism.</p>
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".