Adapting the RIVICE model to frazil ice formation in a small watercourse in eastern Ontario
Bibliographic record
Abstract
Abstract Small communities, located on small watercourses, are threatened by ice flooding. Models can simulate and predict ice flooding events, but have focused on larger watercourses and break-up ice events. Having a model that can predict potential frazil ice events on a smaller scale would allow communities more time to react. Data requirements for ice models include climate data, bathymetry, velocity, depth, and flow. Collecting adequate data for small watercourses can be difficult, and expensive, though correlating site specific short-term data to long-term data sets can be useful in simulating past events, and comparing to current, and future conditions.Climate change will influence ice formation. Changes may improve, or worsen, the potential for frazil ice flooding events. Using a calibrated model, and estimations of future climate change, future ice flooding issues may be identified. The principal goal of this research is to explore the potential for monitoring, modeling, prediction, and options for mitigating ice flood events. The site of interest has experienced numerous ice flood events, and the local municipality has been searching for a reasonable option to predict, and mitigate the potential for flooding, and/or damage from flooding.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".