African lake management initiatives: The global connection
Bibliographic record
Abstract
Abstract There is a global dimension to lake management in Africa and elsewhere that will require a concerted action not only from individual riparian states, but also from regional, continental and global communities. The current global lake threats arise from climate change, regional land degradation and semivolatile contaminants, and share the common feature that the atmosphere is the vector that spreads their impacts over large areas and to many lakes. The Great Lakes of Africa (Malawi, Victoria and Tanganyika) are particularly sensitive to these problems because of their enormous surface areas, slow water flushing rates, and the importance of direct rainfall in their water budgets. Their response times might be slow to yield a detectable change and, unfortunately, their recovery times might also be slow. It is possible for atmospheric effects to act antagonistically to the impacts of catchment change, but antagonistic effects could become synergistic in the future. Improved understanding of the physical dynamics of these lakes, and development of models linking their physical and biogeochemical behaviour to regional, mesoscale climate models, will be necessary to guide lake managers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".