Evaluation of the implications of ice‐jam flood mitigation measures
Bibliographic record
Abstract
Abstract Ice‐jam flood risk management requires new approaches to reduce flood damages. Although many structural and non‐structural measures are implemented to reduce the impacts of ice‐jam flooding, there are still many challenges in identifying appropriate strategies to reduce the ice‐jam flood risk along northern rivers. The main purpose of this study is to provide a novel methodological framework to assess the feasibility of various ice‐jam flood mitigation measures based on risk analysis. A total of three ice‐jam flood mitigation measures (artificial breakup, sediment dredging and dike installation) were examined using a stochastic modelling framework for the potential to reduce the ice‐jam flood risk along the Athabasca River at Fort McMurray. An ensemble of hundreds of backwater level profiles was used to construct ice‐jam flood hazard maps to estimate expected annual damages, using depth‐damage curves for structural and content damages, within the downtown area of Fort McMurray. The results show that, while sediment dredging may be able to reduce a certain level of expected annual damages in the town, and artificial breakup and a dike with a crest elevation of 250 m a.s.l. can be the most effective measures to reduce the amount of expected annual damages.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".