Long term spatial trends in West African monsoon precipitation
Bibliographic record
Abstract
Across West Africa, rainfall is a vital weather component. Its importance in agriculture, the economy, and even governance across the region cannot be overstated due to the nature of the socio-economic vulnerabilities within this region. Therefore, the performance of the region’s economy and its governance depend largely on rainfall distribution directly or indirectly. This study aims to analyze rainfall distribution across the West African region using four indices: the mean rainfall distribution per month, days with rainfall exceeding a 1 mm threshold, days with rainfall exceeding a 5 mm threshold, and the maximum amount of rainfall each year. The CPC Global Unified Gauge-Based Analysis of Daily Precipitation with a resolution of From 1979 to 2020 was used in this study. Trend was determined using the non-parametric Sen’s slope and Mann-Kendall tests. The number of days with rainfall greater than 1 mm and 5 mm showed positive trends between 0 and 0.4 mm/month during the considered months, although the trends were not statistically significant. Locations off the coast of Sierra Leone and Liberia, as well as on the continent of Nigeria, showed statistically significant negative trends. Considering the number of days with heavy rainfall from April to July, we observed a reduced trend in values compared to days with normal precipitation. This implies that although normal rainfall will increase, it will not be as much as heavy rainfall.
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 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 | 0.002 |
| 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".