Research needs for improved dam safety risk management of internal erosion: (Abstract)
Bibliographic record
Abstract
Dams play a central role in the stewardship of Canada's hugely valuable water resources, for which storage, flood control and hydropower generation are the key national interests.There are more than 10,000 dams across the country, most of which are owned by the federal and provincial governments, electric utilities, industrial and mining companies, irrigation districts, and municipalities.British Columbia, like the provinces of Quebec, Manitoba, Newfoundland and Labrador, and also the territory of Yukon, generates almost 90 % of its energy from hydropower sources.Canada is now the world's biggest producer of hydroelectric power, generating 350 TWh/year, or approximately 13 % of global output.Three of the biggest embankment dams in the world, based on reservoir storage capacity, are located in Canada.They represent an enormous investment by Canadian society at-large and, like much of our public infrastructure, these northern-climate embankment dams are ageing.An effect of the ageing process, at susceptible locations, is for water seeping from the reservoir to erode, with time, fractions of soil in the embankment dam.Such internal erosion is probably the greatest, and least understood, risk of failure in embankment dams worldwide.The industry need is to address the scientific issue of spatial and temporal influences on the occurrence of internal erosion in embankment dams, and more specifically: What is the relation between soil type, effective stress and seepage-flow that explains where internal erosion initiates within a zoned embankment dam? What explains the time-rate at which internal erosion continues, and hence determines when internal erosion progresses to constitute a significant risk to a dam?The two research questions are defined by the classic need to develop a theoretical model, calibrate it, verify it, and then validate it against field observations.Drawing upon industry experience with dam safety risk management, and related guidelines for dam engineering practice in Canada, the USA and internationally, we outline a research framework to address the two questions.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".