Freeze-up ice-jam flood hazard assessment and mapping
Bibliographic record
Abstract
Abstract Although ice-jam flooding in northern rivers is generally more severe during ice-cover breakup in spring, ice jams during river freeze-up and mid-winter breakup can also impose high flood hazard in some rivers, particularly those in regions with a maritime climate (e.g. Atlantic Canada). In this paper, we numerically simulate ice-jam flood hazard and carried out the flood mapping of a high ice-jam flood risk community along the Exploits River in Newfoundland where the most severe floods occur from ice jams formed during river freezing. A stochastic modelling approach was used to simulate the processes of ice-jam formation and flooding along the river during freeze-up. This approach uses a deterministic river ice hydraulics model that is run repeatedly within a Monte-Carlo framework. Input values for the parameters and boundary conditions were chosen randomly from frequency distributions. An ensemble of backwater levels was produced from which profiles of exceedance probabilities were calculated. The water level elevations are extrapolated into the floodplain to determine flood depths. This approach is a new method to estimate ice-jam flood hazard and risk stemming from river freezing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".