Advancing and applying Blue Economy in the African Great Lakes
Bibliographic record
Abstract
Abstract The three Great African Lakes (Victoria, Tanganyika, and Malawi/Niassa/Nyasa) are important for the Blue Economy growth of their riparian nations, providing, fisheries and aquaculture products, drinking water, microclimatic buffering, relatively cheap transport means, tourism, biodiversity, employment, and sources of energy (hydropower and oil). Economic growth comes with a cost in the form of pollution (municipal waste, industrial waste, sedimentation, agricultural run-off, land-use issues, etc.). Investments are required to augment benefits from improved regional collaboration to manage fisheries and aquaculture, restocking of certain fish species, strengthen transport, further develop tourism, and conserve biodiversity. Investments are also required to reduce the negative effects of climate change, invasive species, eutrophication, overfishing, waste disposal, polluting materials, oil spills in case of exploitation, and other threats to the well-being of the riparian populations, the profitability of economic activities and ecology of the lakes and their basins. The present paper reviews the various activities to advance the concept of the Blue Economy and highlights the utility and importance of lake management. There are excellent Blue Economy growth options for the three African Great Lakes.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".