Spatial Trend Analysis of Temperature and Rainfall and Their Perceived Impacts on Ecosystem Services in Mau Forest, Kenya
Bibliographic record
Abstract
The Mau Forest Complex is an important ecosystem in Kenya providing many ecosystem services to the local communities. However, its degradation has rendered its ability to deliver its ecosystem services such as climate regulation ineffective. This study's objective was to assess the local climate trend in terms of rainfall and temperature and their perceived impacts on ecosystem services in Mau Forest, from 1984 to 2020. We obtained gridded meteorological data for the study area from the Kenya Meteorological Department and analyzed it using Mann Kendell’s test to identify significant trends in temperature and rainfall. Focus Group Discussions were also carried out to establish the people's perceptions of local climate change and its impacts on ecosystem services. The results of the study indicate no significant trend, p>0.05, in annual rainfall over time. On the other hand, the results show a significant trend, p=3.696e-06, in average annual temperature with approximately 2℃ increase from 1984 to 2020. The local climate change has seen a decline in ecosystem services, mainly water levels and agricultural produce. To cope with and mitigate the effects of climate change, there is need to emphasize establishment of agroforestry woodlots and practice of climate-smart agriculture among the forest adjacent communities.
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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 | 0.001 |
| 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".