Bibliographic record
Abstract
Breakup ice jams often occur suddenly, with little warning. Severe flooding or ice-related damage can result from rapid rises in upstream water levels associated with breakup ice jams. Breakup jam prediction methods that can be used to increase response time are desirable to minimize flood damage, including potential loss of life. A variety of hydrologic and hydraulic models exist to predict open-water flooding, whether resulting from rainfall, snowmelt, or catastrophic events such as dam breaches. However, breakup ice jams result from a complex series of physical processes that cannot currently be described with analytical or deterministic models, hindering the development of prediction methods. Those which do exist are highly site specific and range from simple empirical models to an artificial intelligence formulation. To date, no one model exhibits a clear advantage over the others. This paper provides examples of existing breakup ice jam prediction methods and discusses their potential advantages and disadvantages.Key words: ice jam, breakup ice jam, flood prediction, flood warning, ice jam mitigation.
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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 | 0.001 |
| 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) | 0.001 | 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 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".