Probabilistic method for seismic vulnerability ranking of canadian hydropower dams
Bibliographic record
Abstract
A probabilistic method was developed for ranking Canadian hydropower dams according to their seismic vulnerability. The method is based on the probabilistic seismic hazard at the dam location, the seismic fragility of the dam, and the construction date of the dam. The seismic hazard is represented by the peak ground acceleration of seismic motions at the dam location for a specified probability of exceedance. The seismic fragility of the dams is included through fragility curves, which describe the experience-based probability of the dam reaching or exceeding different damage states as a function of the peak ground acceleration. Different fragility curves are used for different types of dams. The construction periods of the dams are incorporated through approximate factors reflecting the improvements in seismic hazard estimation and dam design. The method was applied to rank a sample of hydropower dams in Canada. These included a range of different types of dams, construction periods, and seismic hazard conditions. The ranking of seismic vulnerabilities is intended to ensure that any possible safety or reliability issues posed by the top-ranked (apparently most vulnerable) dams are raised earlier rather than later.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".