River-ice break-up/freeze-up: a review of climatic drivers, historical trends and future predictions
Bibliographic record
Abstract
Abstract River ice plays a fundamental role in biological, chemical and physical processes that control freshwater regimes of the cold regions. Moreover, it can have enormous economic implications for river-based developments. All such activities and processes can be modified significantly by any changes to river-ice thickness, composition or event timing and severity. This paper briefly reviews some of the major hydraulic, mechanical and thermodynamic processes controlling river-ice events and how these are influenced by variations in climate. A regional and temporal synthesis is also made of the observed historical trends in river-ice break-up/freeze-up occurrence from the Eurasian and North American cold regions. This involves assessment of several hydroclimatic variables that have influenced past trends and variability in river-ice break-up/freeze-up dates including air-temperature indicators (e.g. seasonal temperature, 0˚C isotherm dates and various degree-days) and large-scale atmospheric circulation patterns or teleconnections. Implications of future climate change on the timing and severity of river-ice events are presented and discussed in relation to the historical trends. Attention is drawn to the increasing trends towards the occurrence of mid-winter break-up events that can produce especially severe flood conditions but prove to be the most difficult type of event to model and predict.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".