Hydro-ecological effects of changing Arctic river and lake ice covers: a review
Bibliographic record
Abstract
Freshwater ice is an integral part of the hydrologic regimes of cold environments. It controls the ecology of related aquatic systems and is important economically, through the facilitation of winter transport and via generation of extreme hydrologic events. Given projected changes in future climate, concern has been raised about related changes in freshwater ice. This paper reviews the status and trends in records of lake and river ice around the circumpolar North from traditional observations, remote sensing and paleo-sources. The temporal and spatial variability in trends are evaluated with relation to climatic conditions. Rapid changes experienced in freeze-up and break-up timing for high-latitude lakes, compared to those at more southerly locations, are particularly notable. Also considered are the nature and implications of changes in future freshwater-ice regimes that will have cascading effects on cold-region hydrology and a suite of hydro-ecological conditions including UV radiation receipts, habitat quality and availability, fisheries productivity and contaminant pathways. Overall, the duration and event timing of river and lake ice are proving to be useful indicators of climate change. Considering the scope and significance of ice-cover changes on northern hydro-ecology, a recommendation is made to place more emphasis on long-term and spatially diverse monitoring of freshwater ice.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".