A Stochastic Hydraulic Modelling Approach to Determining the Probable Maximum Staging of Ice-Jam Floods
Bibliographic record
Abstract
There is a need to determine the maximum backwater staging possible from ice jam flooding along high flood risk prone sections of northern rivers. Similar to the probable maximum flood PMF, which is primarily estimated for the most extreme open-water floods, probable maximum floods from ice jamming PMFice can provide upper thresholds of water level elevations so essential for infrastructure designed in and along cold-region rivers. However, the processes for maximum ice-jam flooding are quite different from those of extreme open-water floods which requires river ice processes to be incorporated into the calculation approach. This paper presents a novel method for estimating the probable maximum staging from ice-jam floods. The method is based on the implementation of a deterministic hydraulic model that mimics ice jam processes and is nested in a stochastic framework to carry out Monte-Carlo simulations to randomise parameter and boundary condition value inputs for many hundreds of simulations. This stochastic approach provides the frequency distributions of many of the boundary conditions used to force the river ice hydraulic model. The stochastic modelling framework yields ensembles of backwater levels from which the maximum level provides an indication of the probable maximum staging possible, the PMFice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".