Freshwater ecosystem transitions due to artisanal sand mining in Rwanda, Africa
Bibliographic record
Abstract
Artisanal small-scale mining (ASM) of sand, gravel and crushed stones plays an economically important role through its value as a 'development mineral' for a growing population in sub-saharan Africa. The extracted material is used in developing and expanding urban areas and infrastructure and provides income for the population involved in the sector. However, the extraction of aggregates has shown to have large and often complex ecological and socio-economical consequences with potential significant health effects on the miners and the environment in which the mining takes place. Here we show that ASM in a river channel in central Rwanda causes a systemic shift in freshwater biodiversity by changing species assemblages from being riverine towards communities representing standing waters. Based on 101 point samples, we find that ponds created due to mining activities act as habitats for freshwater insects associated with wetland habitats. Furthermore, these mining ponds did also act as breeding sites for mosquitoes and thereby potentially increase the presence of vector borne diseases such as malaria. These findings show how ASM can generate a landscape level shift in freshwater biodiversity and introduces the apparent paradox that while aggregates are critical building blocks in mitigating malaria transmissions and prevalence through improved housing, the mining practices unwillingly can create new breeding ground for malaria mosquitos, thus increasing the risk of malaria spreading to nearby communities.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".