Modeling of River Ice Jams for Flood Forecasting in New Brunswick
Bibliographic record
Abstract
Ice jams cause major flooding and severe damages to communities and infrastructure along the Saint John River. As the climate changes within the Saint John River Basin, ice movement and the potential for ice jam damages may increase. There is a growing need to develop capability in forecasting and analyzing ice-jam-related flood events. The HEC-RAS model has been applied to recent events along the international Saint John River from Dickey, Maine, USA, to Grand Falls, New Brunswick, Canada where calibration data and local observers ’ reports on ice conditions are available. The model has been applied to various situations including ice jams formed during freeze-up, mid-winter thaw and during spring breakups. Difficulties encountered during model runs and methods to overcome these are discussed. In general, it is possible to obtain good agreement between computed and measured water levels by adjustment of various model coefficients such as the friction angle and the critical ice transport velocity. Model performance was improved considerably when a set of additional transects were generated by interpolation to reduce the transect spacing. Future work will include linking the model with remote-sensing imagery and developing operational procedures to forecast flooding associated with predictable ice jams.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".