Portfolio Risk Assessment Using Risk Index
Bibliographic record
Abstract
This Technical Note provides detailed \n information on the Brazilian risk classification system \n using the RI approach and the Indian RI system for the \n initial risk screening of a large portfolio of existing \n dams. Annex A provides basic information about the RI \n approach used in Quebec, Canada, for its dam classification \n system. These RIs are used for prioritization of required \n remedial works and other safety requirements. It should be \n noted, however, that RI is also a basic tool for preliminary \n level risk analyses for portfolios of dams and initial \n screening of risky dams, which may need to be supplemented \n by more advanced methods, depending on the type and \n potential risk of the dams. Because RI largely relies on \n visual inspection of the dams’ conditions, some critical \n failure modes could be missed. underestimated, or \n overestimated. In the higher risk cases, or whenever deemed \n appropriate, more detailed risk analyses, such as potential \n failure mode analysis (PFMA), can fill some of the gaps.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".