A Sustainability-Based Framework for Assessing Dam Failure Impacts: Integrating AI-Assisted Indicator Extraction and Application to the Fundão Case
Bibliographic record
Abstract
Dam failures generate long-lasting environmental, social, and economic consequences that challenge sustainable recovery and governance. Existing assessment approaches typically focus on single domains or short-term impacts, limiting their ability to capture interdependencies and long-term effects. This study develops a Sustainability-Based Dam Failure Impact Framework (SDFIF) to systematically evaluate short- and long-term consequences across environmental, social, and economic pillars. The framework integrates a PRISMA-guided literature review (88 sources), AI-assisted indicator extraction, and hierarchical classification, resulting in 328 validated indicators. The framework was applied to the 2015 Fundão tailings dam failure, revealing that social and economic impacts (normalized scores >0.9) persist beyond partial ecological recovery, indicating sustained livelihood disruption, cultural loss, and community vulnerability. These findings highlight the importance of incorporating temporal persistence and cross-domain interactions in post-disaster assessment. The SDFIF provides a transparent and adaptable tool to support recovery monitoring, regulatory evaluation, policy development, and sustainability-oriented risk management in dam governance.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".