Reaching a breaking point: How is climate change influencing the timing of ice breakup in lakes across the northern hemisphere?
Bibliographic record
Abstract
Abstract The duration of seasonal winter ice cover has declined in many mid‐ and high‐latitude regions around the world as climate continues to warm. We obtained data on lake ice breakup dates, air temperature, precipitation, and large‐scale climate oscillations for 152 lakes across the northern hemisphere from 1951 to 2014. Ninety‐seven percent of study lakes exhibited earlier ice breakup trends. Forty‐six percent of the variation in ice breakup trends was driven by spring air temperatures and elevation across the northern hemisphere. However, changes in ice breakup have not always been in a gradual or linear pattern. Using the sequential T‐test analysis of regime shifts, we found evidence of abrupt changes in mean ice breakup for 53% of lakes with shift years identified between 1970 and 2002. Concurrently, we found abrupt changes in mean spring and winter air temperatures, winter precipitation, and large‐scale climate oscillations that occurred either the same year or 1 yr prior. Earlier ice breakup and the shortening of the ice season will have consequences for winter heritage, local economies, and lake ecosystems around the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".