Estimating Seasonal and Interannual Variations in Precipitation in Rural Eastern Africa: A Case Study in Longido, Tanzania
Bibliographic record
Abstract
Access to water is a limiting factor for development in many semi-arid regions, contributing to food insecurity and environmental stresses on the local population. Additionally, some rural areas still have limited quantitative data on weather and associated rainfall patterns. This study analyzes ground meteorological data from a station installed at Longido, Tanzania and performs time series decomposition modelling of complementary Integrated Multi-satellite Retrievals (IMERG) data to quantify the amount, distribution, and variability of this essential resource. The seasonal rainfall pattern at Longido is bimodal with a large peak between March and May and a smaller peak between October and November. Interannual variability in rainfall is only weakly correlated with El Niño and the Indian Ocean Dipole indices; however, the highest observed rainfall does occur in a year with numerous simultaneous storms in the southern Indian Ocean. This analysis will help improve water management planning in the locality and points to a need to promote and support water storage as a method to meet the needs of the local population
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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 | 0.001 | 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".