Monitoring climate change and anthropogenic pressure at Lake Tanganyika
Bibliographic record
Abstract
The African Great Lakes are under threat from global and local environmental challenges including climatic change, water pollution and overfishing. To address those issues, managers need observations based on regularly monitored environmental indicators. However, environmental monitoring of the African Great Lakes is often lacking or not based on harmonised methods. The present manuscript is a case study based on Lake Tanganyika, impacted by climate change and anthropogenic pressure affecting water quality, fisheries and biodiversity changes. The implementation of environmental monitoring has often not been continuous or standardised among bordering countries. This prevents managers from taking data-based decisions and opens a risky field where speculation may overcome a rational approach. Long-term monitoring observations are essential to guide management measures to adapt to climate changes and decrease, whenever possible, unfavourable human impact on the Great Lake environment. A regionally standardised long-term monitoring programme is proposed. The sustainability of such monitoring requires that it remains inexpensive and focuses on a few essential parameters. Its strength would be its uninterrupted implementation. Setting up a long-term integrated monitoring programme is also a goal of the Lake Tanganyika Authorities (LTA) with mandated national authorities and stakeholders. A Lake Tanganyika Regional Integrated Monitoring Programme (LTRIEMP) needs to be widely encouraged and supported to ensure its sustainability. General principles from the Lake Tanganyika case study could be useful to develop a wider harmonised sustainable long-term regional monitoring network of the African Great Lakes in a multi-lakes collaborative approach.
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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".