Bibliographic record
Abstract
Abstract The hydrology of almost 60% of rivers in the Northern Hemisphere is affected by ice for at least a portion of the winter. At the higher latitudes, river ice is the major controller of most fluvial processes. Although traditionally the pursuit of hydraulic engineers, hydrologic studies of river ice have broadened greatly over the last decade. Initially, this was driven by recognition that many hydrologic extremes produced by river ice, such as floods and low flows, exceed those of the more intensively studied, open‐water period. Added recognition that river ice strongly influenced other fluvial disciplines, such as geomorphology and aquatic ecology, increased its scientific visibility. This manuscript reviews the basic physics of river ice; evaluates its effect on various hydrologic processes and events from freeze‐up to breakup; and discusses its extended role in affecting sediment transport/erosion, river morphology and a number of key ecological processes including geochemical mixing and habitat modification. Arguments are made for increased focus on river‐ice hydrology given its significance to physical and ecological processes, and the growing need to understand the effects of climate change and flow regulation on cold regions rivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".