Global Warming Status in the African Continent: Sources, Challenges, Policies, and Future Direction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Africa is the second largest continent after Asia, having a larger than 30 million km 2 area. Doubtlessly, one of the biggest ecological and societal problems of the twenty-first century is climate change. Since the early 1970s, it has been clear that Africa is already experiencing the effects of climate change, and it has given rise to a wide range of new and unusual phenomena, such as rising temperatures, poor agricultural output, extreme different weather scenarios, and the spread of disease, among other things. Therefore, the current review aims at screening the impact of climate change on agricultural sector, human health and food security in Africa compared to the other continents, evaluating the change projections in future and highlighting the role of African leaders in mitigating and adapting to these effects. Artificial intelligence, remote sensing, and high-tech algorithms were applied to analyze these effects. Historical data were downloaded in near real-time from January 2009 to the present from the FAO Water Productivity Open-access portal WaPOR and Terra Climate datasets on Earth Engine platform. Assessment process was performed using Google Earth Engine, whereas future data were downloaded from WorldClim 2.1. We used 2021–2040 timelines and two scenarios: SSP245 and SSP585. For the SSP and timeline, we downloaded four versions, based on four different global circulation models (GCMs): IPSL-CM6A-LR (France), MRI-ESM2-0 (Japan), CanESM5 (Canadian), and BCC-CSM2-MR (China), to reflect the uncertainty among GCMs. We averaged future projection of each variable and SSP across four GCMs to decrease the uncertainty connected with a particular GCM. We presented the averaged results as maps. Annual precipitation totals were significantly above average in Central and East Africa, while under SSP 245 scenario, Madagascar would experience high rainfall. The highest temperature anomalies were seen in parts of the Greater Horn of Africa, western equatorial regions, and the north-western part of the continent. Minimum and Maximum temperature predictions showed that Africa would experience harsh temperatures than previously recorded in the historical years. A high average maximum temperature is predicted across the sub-Sahara Africa, South Africa, Somalia, and Madagascar under SSP 245 and SSP 585. The MCD64A1 dataset tagged in Earth Engine was used to classify forest fire risk in Africa. Analysis revealed that the highest fire risk was recorded in Savannah in tropical and subtropical Africa. Further, changes in rainfall and increased temperature leading to increased evaporation would directly reduce runoff levels and recharge groundwater which in turn will have negative effects on biodiversity, agriculture, and food security. Notably, African leaders have played positive role in the recent climate negotiations and bright climate initiatives have been emerged. Hopefully they will solve the climate crisis across the continent.
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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 | 0.000 | 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 it