Global Warming and Water Resources Variability in the Maritime Region of Togo (West Africa)
Bibliographic record
Abstract
One of the most important climatic phenomena of our planet on the threshold of this third millennium is undoubtedly that of global warming. This phenomenon, whose impact on the global environment is disastrous, has been attracting the attention of the international community since the end of the last century. Global warming can be observed on a global scale, but with regional and local signatures. In Togo, the Maritime region which houses the bulk of industrial activities (90%) and densely populated (40% of the national population), would be more exposed to the effects of global warming than all the other regions of the country. The current study uses meteorological and hydrological data to calculate statistical indexes which show the evolution of temperatures and water resources variability. The aim is to detect the signs of global warming and the variability of water resources in order to envisage better adaptation strategies. The results of the analysis of climate data for a long enough period (30 to 50 years) demonstrated a change on rain distribution after 1960 which is prejudicial to rainfall agriculture practiced by about 90% of Togolese crop growers, and an augmentation of the average temperature about 2.2 degree celsius for Lomé and 1.7 degree celsius for Tabligbo.
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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) | 0.000 | 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 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".