Influence of Fundão Tailings Dam Breach on Water Quality in the Doce River Watershed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Fundão dam (Minas Gerais, Brazil) breach resulted in the transport and deposition of Fe mine tailings and debris for approximately 670 km along the Doce River watershed. Following the event, an extensive water quality monitoring program was implemented. The results generated by this program were used to assess the temporal and spatial impacts of the event on water quality. Data from several sampling sites situated along affected watercourses collected from 6 November 2015 to 27 September 2017 were evaluated. The sampling area was grouped into 4 zones delineated by hydropower plant dams located along the Doce River watershed and divided into 5 distinct time periods related to hydrological seasonality. Data were also compared to the Brazilian standards of water quality and available pre-event conditions. Principal component analysis followed by analysis of variance (ANOVA) tests were performed to evaluate the observed temporal and spatial trends and patterns. The results indicated that concentrations of 58 water quality parameters increased with the arrival of the tailings wave released by the breach, generally exceeding pre-event and regulatory levels. Persistent or seasonally recurring concentrations were observed for 30 water quality parameters. Concentrations of total and dissolved forms of Fe, Al, Mn, total P, total suspended solids, and turbidity tended to increase during the wet season and decrease during the dry season, this trend being more pronounced close to the dam. The water quality changes were more pronounced immediately after the arrival of the tailings wave, gradually decreasing over time and returning prebreach conditions, but fluctuated seasonally in response to the natural variation in river flow. Integr Environ Assess Manag 2020;16:583-595. © 2020 SETAC.
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.
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 it