A glance at 25 process-based hydraulic models for river ice
Bibliographic record
Abstract
Many hydraulic models for river ice have been developed over the past several decades (e. g., CRRISP1D/2D, RIVICE, ICEJAM, MIKE-Ice, River1D/2D, JJT, VARY-ICE, …). Their purposes were to improve our understanding of river ice phenomena and better anticipate their timing, extent, and flood risks over the short term (for operational purposes) and the long term (in the context of a changing climate). Twenty-five such models are briefly described in this paper, with information on the processes that are captured by each model, their application (usage), whether or not they are in the public domain, and sources for more information. The processes are divided into various classes: water cooling, frazil ice generation, border ice, anchor ice, ice cover formation, thermal growth and decay, ice cover break-up, ice dynamics for ice jamming/bridging, and water seepage through the keel of the ice jam. Most models are one-dimensional, i. e., they do not account for variations along channel width or depth. Model performance is not discussed – the intent of the article is, instead, to report on the variety of models described in the river ice literature and provide the readership with a summary of what they are.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".