Bibliographic record
Abstract
In Canada's electrical energy sector, the destruction of hydroelectric and control dams as a result of terrorist attacks could result in catastrophic floods of downstream communities and strains on the electricity needs of the Canadian society. The energy sector is viewed as a potential target by terrorists and environmentalist groups seeking to cause harm to the public or to obtain media publicity. In order to provide insight into the effects of explosives on dam infrastructure and the possible mitigation measures to minimize the potential damage from such attacks, this paper reviewed the effects of attacks with explosives on dams, their consequences on the dam infrastructure, and the recoverability from the attacks. The review was presented according to the two general classes of dam types, earth embankment dams and concrete/masonry dams. The earth embankment dams that were discussed included the Sorpe dam and the Peruca dam. The concrete and/or masonry dams that were identified and discussed included the Mohne dam; Eder dam; Ennepe dam; Hwachon dam; Dnjeprostroj dam; and other dams around the world in conflict situations. It was concluded that dam infrastructure represents a vulnerability with serious consequences that terrorist groups could exploit to cause harm or gain media exposure and that understanding the effects of various quantities of explosives on dam infrastructure is critical to establishing guidelines to protect dam infrastructure from terrorist attacks. 13 refs., 11 figs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".