A sensitivity analysis on the hydrometeorological and ice parameters used for estimating ice forces: A case study of the Sartigan ice control structure
Bibliographic record
Abstract
AbstractThe design of an ice control structure (ICS) is intended to reduce the risk of ice-induced floods along a specific river reach. Typically, ICSs consist of a weir, which slows down the flow, on top of which equally spaced piers intercept floating ice blocks. To properly design these ICSs, estimating the dynamic ice forces exerted on them is essential. The current design norms are to use the estimation of ice forces on bridge piers (CSA, 2019). Nevertheless, the relative effects of the different hydrometeorological parameters on the estimated ice forces on ICS have never been investigated. Therefore, the main objective of this study is to perform a global sensitivity analysis of ice characteristics and hydrometeorological parameters affecting the estimation of ice force on ICSs. For this purpose, the Sartigan ICS (referred to as Sartigan Dam) is used as a case study. The Sartigan Dam is located on the Chaudière River, 3 km upstream of the Town of St-Georges, Quebec, Canada and consists of a 7 m high weir and 12 piers distanced every 6.1 m forming 11 openings. The results show that wind speed and ice cover length play a significant role in ice forces when a static ice cover is present with no bottom gates opened. For the case of an ice jam, hydraulic parameters such as flow and water surface slope, as well as ice thickness and cover length, become important in estimating the ice forces. Finally, in the case of an ice jam with one bottom gate open (i.e., active management of the dam), the impact of the hydraulic parameters and ice characteristics are reduced, but the effects of the wind speed and ice cover length increased for the calculated ice forces.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".