Climate Change Drives Increases in Extreme Events for Lake Ice in the Northern Hemisphere
Bibliographic record
Abstract
Abstract Extreme climate events can have significant consequences on ecosystems and by extension human populations. Over 50 million of the world's lakes typically freeze each winter, and the absence of winter ice cover, in lakes where ice has historically been present, can be characterized as an extreme event. We quantified the effects of extreme climate events on lake ice cover using 78‐year ice records from 122 lakes to show that (1) extreme ice‐free years are becoming more frequent and severe, (2) winter air temperature is a significant predictor of ice cover that was driven by large‐scale climate oscillations, (3) extremes in temperature are closely related to extremes in ice cover, and (4) ice‐free years are forecasted to result in significant loss of ice‐cover in the future. Without drastic reductions in carbon emissions, we can expect the widespread loss of lake ice cover could have significant socioeconomic and biological implications.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".