Assessing the Long-Term Changes in Selected Meteorological Parameters over the North-Rift, Kenya: A Regional Climatology Perspective
Bibliographic record
Abstract
Understanding long-term trends in climatic variables is essential for assessing climate change impacts on regional ecosystems and human livelihoods. A regional analysis of climatic variables over some domains is inevitable due to their geographical location and importance to the agricultural sector. Due to the aforementioned demands, the current study analyzes, trends in precipitation (from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)), and minimum and maximum temperatures (from TerraClimate) over the North-Rift region of Kenya for over thirty (30) years using satellite data. The seasonal decomposition analysis was performed for each variable to explore the trends and residual components. The findings by the current study indicate that most counties, have experienced enhance precipitation which corresponds to a declining diurnal temperature from 2019 onwards. The seasonality component reveals repeated patterns or variations occurring at steady intervals within each region's data, hence suggesting a distinct regional seasonal trend in the selected meteorological parameters over time. Basically, all counties have reported a relatively constant variability in both maximum and minimum temperatures during the study period except from 2017 onwards where significant variability in the two properties is recorded. In conclusion, the foregoing results that the selected climatic variables exhibit significant spatiotemporal and interannual variability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".