Applicability of 2-D modeling for forecasting ice jam flood levels in the Hay River Delta, Canada
Bibliographic record
Abstract
Ice jam floods can present an annual threat to communities adjacent to rivers, especially those situated at river confluences, island or deltas. The objective of this study was to determine whether 2-D modeling might be used to predict expected ice jam flood elevations for such multi-channel systems. The Hay River delta was selected as the demonstration site for this study, and 2-D modeling was employed to calculate ice jam flood levels expected for varying inflow discharge conditions. The River2D model was calibrated for historical ice jam profiles covering a range of discharges from 268 to 1000 m 3 /s and a relationship was developed to predict the flow split down the East and West Channels based on the inflow discharge to the delta. Also, ice jam rating curves were developed at 0.5 km intervals along each channel facilitating the development of an ice jam profile prediction tool for use by the Town of Hay River.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".