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Record W4379055202 · doi:10.1007/s41742-023-00534-w

Global Warming Status in the African Continent: Sources, Challenges, Policies, and Future Direction

2023· article· en· W4379055202 on OpenAlexaboutno aff
Heba Bedair, Mubaraka S. Alghariani, Esraa Omar, Quadri A. Anibaba, Michael Remon, Charné Bornman, Samuel Kiboi, Hadeer Abdulrahman Rady, Abdul-Moomin Ansong Salifu, Soumya Ghosh, Reginald Tang Guuroh, Lassina Sanou, Hassan M. Alzain

Bibliographic record

VenueInternational Journal of Environmental Research · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsnot available
FundersTanta UniversityScience and Technology Development Fund
KeywordsGlobal warmingEnvironmental planningEnvironmental scienceClimate changeEnvironmental resource managementGeographyEarth scienceGeologyOceanography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.077
GPT teacher head0.338
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations71
Published2023
Admission routes1
Has abstractyes

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