Experimental Analysis on Hanging Dam Formation and Evolution
Bibliographic record
Abstract
Hanging dams are thick accumulations of frazil ice beneath an existing ice cover that are formed during the freeze-up period at locations where a fast-flowing river section enters a section with relatively low velocity. Hanging dams can have a substantial impact on the hydraulics of an ice-covered river. This paper presents an experimental study on hanging dam formation and evolution conducted using a laboratory physical model of a river issuing water into a relatively large reservoir using simulated frazil ice and a simulated ice cover. The incoming ice supply rate and the approach Froude number of the river are the two parameters that have an impact on the hanging dam formation with respect to several physical characteristics of the hanging dam. Hanging dam erosion was observed by increasing the approach Froude number of the river after a hanging dam had already formed. Both the formation and erosion of the hanging dam were qualitatively compared with field observations of hanging dam occurrences using satellite imagery and hydrometric data to support the applicability of the experimental results to a field scenario. The results presented in this paper comprise the first published qualitative laboratory data on hanging dam formation, helping to improve our understanding of the fundamental mechanisms of hanging dam formation and evolution.
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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 | 0.000 |
| 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) | 0.002 | 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".