Hydro-climatic drivers of mid-winter break-up of river ice in western Canada and Alaska
Bibliographic record
Abstract
The mid-winter break-up of a competent river ice cover can cause ice jamming and flooding, which can have profound impacts on the structure and strength of the ice cover. This research identifies 52 mid-winter break-up events in western Canada (1950–2008) and Alaska (1950–2014) and evaluates the hydro-climatic drivers including temperature and precipitation. The identified mid-winter break-up events are primarily located in the temperate zone, defined as the region between 400 and 1,000 winter (December–February) freezing degree-days. Further delineation by terrestrial biome revealed considerable variability in hydro-climatic triggers, particularly the role of freeze-thaw days (Tmax > 0 °C and Tmin < 0 °C) in Tundra and Boreal Forest/Taiga biomes and short-term (3-day) warming events in Temperate Coniferous Forests and Temperate Grasslands, Savannas, and Shrublands. The classification of 5-day sequences of mid-tropospheric circulation indicates that a persistent trough of low-pressure over Alaska and the North Pacific is the dominant pattern preceding mid-winter break-ups. Furthermore, the trough is stronger for events in British Columbia and Alberta compared with Alaska and the Yukon. The results of this research improve our understanding of the hydro-climatic conditions that generate mid-winter break-up events in western Canada and Alaska and will aid in the prediction and risk management of such events.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".