Effects of mine tailings on aquatic macroinvertebrate structure within the first year after a major dam collapse
Bibliographic record
Abstract
Context The collapse of a tailings dam in Brumadinho (Brazil) is considered one of the largest mining disasters worldwide. The mine tailings polluted the water and sediment of the Paraopeba River downstream of the collapsed dam. The effects of the tailings on biological communities remain unknown. Aims We evaluated the effects of the tailings dam collapse on aquatic macroinvertebrate assemblages in the Paraopeba River and highlighted a potential bioindicator for the cumulative effects of the mine tailings spill. Methods We sampled the macroinvertebrates upstream and downstream of the collapsed dam during the first dry and wet seasons following the collapse. Key results We found that turbidity (likely non-related to the tailings) negatively affected the macroinvertebrates’ abundance, but the richness was negatively affected by the presence of the mine tailings. The riparian land use negatively affected the macroinvertebrate richness and composition. We identified Helicopsyche spp. as a bioindicator. Conclusions We provide circumstantial evidence of the effects of mine tailings on aquatic macroinvertebrates, suggesting that it may have affected their richness and caused the loss of Helicopsyche spp. in the most affected sites. Implications We suggest that the richness and Helicopsyche spp. are potential biomonitoring tools for evaluating the effects of the tailings dam collapse on the macroinvertebrate assemblages.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".