Analysis of the rainfall variability and change in the Republic of Benin (West Africa)
Bibliographic record
Abstract
The temporal variation and trends of annual rainfall distribution in Benin were examined using data from 1940 to 2015 at six meteorological stations and three raingauges stationed throughout the country. The nonparametric modified Mann-Kendal (MK) and Levene tests were applied to detect trends and heteroscedasticity, respectively. For six of the time series, no significant trends were detected. A Bayesian multiple change points detection approach was applied to the rainfall time series, and most (six of nine) exhibited abrupt change points, corresponding to the alternation between wet (before 1968 and after 1990) and dry (1969–1990) periods. No significant trends or breakpoints and changes in the variance were observed for the spatial average rainfall time series. Seven modified MK trend tests were applied; the trends are affected by the selected MK method and rainfall statistics. Oceanic and/or atmospheric influences on the rainfall in Benin were examined by investigating the correlation between the precipitation time series and several indices. Negative seasonal correlations were determined for the North Atlantic Oscillation, Pacific Decadal Oscillation and Niño3, while positive seasonal correlations were observed for the Southern Oscillation, Antarctic Oscillation and Dipole Mode Index.
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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 | 0.000 |
| 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".