Environmental perturbations and anthropogenic disturbances determine mollusc biodiversity of Africa’s explosive Lake Kivu
Bibliographic record
Abstract
Lake Kivu (Rwanda and Democratic Republic of Congo) is known to be unique among the African Great Lakes due to its peculiar history as a volcanic barrier lake and the frequent environmental pertubations caused by limnic eruptions. This lake is a major resource for riparian people but is also characterized by a depauperate fauna. For molluscs, available information is scarce and spatio-temporally restricted. We studied the freshwater molluscs of the lake and its tributaries and their biogeographical affinities, for the first time via genetic characterization. Our study revealed that the malacofauna of Lake Kivu, though admittedly poor compared to other African Great Lakes, is significantly more diverse than previously anticipated. The occurrence of living populations is restricted to a narrow fringe of littoral substrates, but some of the total of eight species occur much deeper than the immediate margins of the lake, i.e. down to a maximum of 15 m. The fauna displays ‘Nilotic’ biogeographic connections though widespread forms dominate. Differences in diversity occur in the North vs. South of Lake Kivu in species richness and abundance measures. This pattern can be attributed to recent volcanic eruptions and geochemical stressors in the north, but also to current and ongoing anthropogenic effects. A detailed study of schistosomiasis and fasciolosis with high spatial resolution along the local communities and their livestock is recommended since several potential intermediate host snails are present in Lake Kivu and its tributaries.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".