Frazil ice events: Assessing what to expect in the future
Bibliographic record
Abstract
This article addresses the question: What is expected from frazil ice activity in rivers, taking into account the changing climate?It begins with an overview of what frazil ice is and what is required for the occurrence of frazil ice events, namely a supercooled water column.Methodologies to anticipate frazil ice events in the short term are based on air temperature and water discharge, underlining the significance of these two parameters for any predictive methods.Longer-term approaches, calibrated against past events (hindcasting), are used to anticipate frazil ice activity into the future, with indicators such as frazil ice risk, water temperature and frazil volume.Any of these approaches could conceivably be applied to frazil-prone river stretches.To assess climate impact, each location should be treated separately.River ice dynamics can lead to the formation of a hanging dam, a frequent outcome of frazil ice generation in the early winter, causing flow restriction.Flood modeling and forecasting capabilities have been developed and implemented for operational use.More frequent mid-winter breakups are expected to extend the occurrence of frazil ice events into the winter monthsthe prediction of these will require climate model output to adequately capture month-to-month variability.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".