Bibliographic record
Abstract
In the last few years, the standards and design practices used for retention structures (dams) have been adjusted to prevent these structures from being damaged by natural disasters. Some older dams, formerly considered safe, do not meet the current standards, causing safety concerns to all groups responsible for these structures. The risk analysis approach is most commonly used for evaluating the necessity, urgency and cost of measures necessary to bring or to maintain the dams up to present-day standards. There are several methods for assessing dam risk. These methods are complementary and used together they cover all aspects of a risk study: analysis, assessment, management and risk minimization. When applied together, they become a reliable decision-making tool for the structure management team when regarding intervention on structures. Polytechnic School of Montreal has developed a model, CERBERE (French acronym for control of the risk state of water-retaining dams), that automates ten methods used to evaluate dam risk. The methods analyze, assess and manage the dam risk. The results obtained from each method are saved in a database and interpreted in a comparative analysis. The degree of risk to the relevant dam is presented in a visual format and through a series of synthesis tables. Using these results, the dam management team can make informed decisions in order to maintain the conformity of the relevant structures with existing standards. This presentation will demonstrate how this model systematize and accelerate the risk assessment process. The methodology used will be illustrated by an application to a hydraulic structure located in the province of Quebec. © 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved. Web: www.witpress.com Email witpress@witpress.com Paper from: Hydraulic Information Management, CA Brebbia and WR Blain (Editors). ISBN 1-85312-912-7
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".