A Survey of the Geographic Area, Altitude, Coastline, and Climate of African Countries and Regions: Implications for Africa’s Development
Bibliographic record
Abstract
This study examines the area, altitude, coastline, and climate of African nations. Africa’s 1.448 billion people in 2023 accounted for 18.14% of the world total of 7.979 billion, and its landmass of 30.32 million sq km, is 20% of the world total of 148.94 million sq km. Africa’s total coastline of 40,188 km is 11.3% of the world total of 356,000 km. Of Africa’s total area of 30,319,532 sq km, 574,393 sq km (2% excluding South Sudan) is water. There are 12 nations in Africa, each with an area of 1 million sq km or more. There are 16 nations in Africa with an average elevation ranging from over 1,000 meters to over 3,000 meters; and 17 nations with a peak elevation level of 3,000 meters or higher. Of 40 nations with hottest day temperature data, 5 (12.5%) are below 100 degrees; and 35 (87.5%) have figures ranging from 101.84 degrees to 124.34 degrees. Twenty-two nations (55% of 40) have coldest day temperatures in the 30s or lower; and 15 (37.5%) nations with temperatures of 32 degrees or less. There are 12 nations in Africa with average annual humidity figures of 60% or less. The study recommends that the African union lead the way in utilizing Africa’s strategic natural resources for its development. The study recommends that the African Union must work with the United States to ease the transition process for Black people returning to Africa from the United States in utilizing their expertise and wealth for Africa’s development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".