Methods for Postdisaster Impact Assessment: A Case Study of the Impacts of the Fundão Dam Failure on Terrestrial Species Threatened with Extinction
Bibliographic record
Abstract
The Fundão tailings dam held back approximately 50 million cubic meters of tailings from an Fe ore mine in the state of Minas Gerais, Brazil. The dam failed on 5 November 2015, releasing tailings into the Rio Doce watershed and causing an environmental disaster in a biodiversity hotspot. To guide conservation action following the event, Brazilian authorities demanded an impact assessment focused on terrestrial species threatened by extinction. Postdisaster impact assessment is substantially more challenging than predevelopment impact assessment. Predisaster baseline data were sparse, and much was unknown about how the Fundão dam failure impacted terrestrial species threatened with extinction. Baseline reconstruction and impact pathway validation and characterization was critical. Ecosystem reconstruction revealed that 1580 ha of terrestrial ecosystems were destroyed, including approximately 480 ha of Atlantic rainforest. Collaboration with local experts identified 346 species of vertebrates, invertebrates, and vascular plants threatened with extinction that were impacted or potentially impacted. Species composition and impacts varied within 4 distinct segments of the Rio Doce watershed. Nine potential impact pathways were identified using data and literature review combined with information provided by regulators, experts, and media. Not all were valid. Validating and characterizing each pathway within each watershed segment involved data evaluation, an ecological risk screening, and field assessments. Impact pathway evaluation proved critical to dispelling misconception, accurately understanding postdisaster impacts, and directing conservation action; the importance of this step cannot be overemphasized. Integr Environ Assess Manag 2020;16:676-680. © 2020 SETAC.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".