Modelling the SM-3 dam, Quebec, Canada
Bibliographic record
Abstract
The analysis of dam behaviour is an important issue within engineering practice. It is important to carry out a good interpretation of the data related to dams, whose materials should be characterized as well as their behavior in the dam. In this way, their behaviour can be properly understood and used in the future dam design and construction. Likewise, it is very important to conduct a thorough study of the factors affecting the behaviour of embankment dams, because it has to be ensured their integrity to avoid any catastrophe. In this Thesis, an earth and rockfill dam (Saint-Marguerite 3 dam), built in Quebec (Canada) between 1996 and 1998, is analyzed. (...) This Thesis presents the interpretation and a numerical analysis of the dam by the finite element method. (...) Starting from the data available and laboratory tests, which were performed on the materials forming the dam, the calibration of the parameters was carried out. Once the parameters were estimated, the construction, impoundment and operation of the dam were modeled. The calculated results are discussed and compared with field measurements in terms of displacements of the upstream and downstream rockfill shells. Finally, there were exposed the conclusions that have been drawn from the overall study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".