Evaluating dam safety in Brazil: a comparative analysis of international classification systems
Bibliographic record
Abstract
ABSTRACT Dams provide essential services to society, but the consequences for downstream communities and the environment can be devastating when safety measures fail. This investigation examines Brazil’s dam safety policies, focusing on classification systems and emergency preparedness. Data from 2022 reveals serious shortcomings: over 14,000 dams are unclassified by Risk Category (RC), and more than half lack a Potential Hazard Associated (PHA) rating. Of the 1,235 dams classified as high risk, only 106 have an Emergency Action Plan (EAP). The study compares Brazil’s system with international frameworks from institutions such as ICOLD, FEMA, USACE, and countries including Portugal, Spain, New Zealand, Australia, South Africa, Argentina, and Canada. While Brazil’s approach aligns with global standards, critical improvements are needed. The study proposes practical adjustments to enhance hazard classification and emergency planning. Future research could explore these proposals through case studies to better guide policy and protect lives.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".