Trends and Variability of Temperatures in the Eastern Province of Rwanda
Bibliographic record
Abstract
ABSTRACT This study investigates air temperature trends and variability over the Eastern Province of Rwanda and its derived near‐homogeneous zones for 1983–2021. Near‐homogeneous zones are obtained using the K‐means clustering method to classify annual long‐term means of rainfall and minimum, maximum and mean temperatures from 570 grid cells in the Eastern Province. Changes in monthly, seasonal and annual minimum, maximum, and mean temperatures are computed with a 95% confidence interval using a dynamic linear state‐space model. This model effectively captures temporal patterns by linking hidden states that evolve over time to observed measurements while accounting for random fluctuations. Additionally, temporal variability is assessed using standard deviation. In Eastern Rwanda, annual minimum and mean temperatures have risen to 2.95°C (confidence interval: [1.64–4.45]) and 1.87°C (confidence interval: [0.61–3.19]), respectively. A significant increase in seasonal minimum temperature is observed in all seasons, the June–July–August season presenting the highest value of 3.37 [1.75–4.81]°C. The seasonal mean and the annual maximum temperature have not significantly changed. Minimum temperature displays notable nonlinearity in its time‐varying trends, remaining relatively stable during 1990–2010 before experiencing a pronounced warming trend thereafter. Over the three identified zones (1. Northwestern, 2. Central and 3. Southeastern), a significant increase in seasonal and annual minimum temperature is observed in the Northwestern and Southeastern zones. The highest increases are in the Northwestern zone, June–July–August season having 4.07 [2.26–6.08]°C. The seasonal and annual minimum, maximum and mean temperatures vary relatively little, with a standard deviation of less than 1°C in all zones. The seasonal and annual minimum and mean temperature increase over the Northwestern zone is dominant over the Eastern Province. This study identifies climate change vulnerable areas in Eastern Rwanda, offering vital insights to guide policy and decision‐makers in supporting affected communities while enhancing resilience and informing future climate research.
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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".