Quantifying the Trends and Drivers of Ice Thickness in Lakes and Rivers across North America
Bibliographic record
Abstract
Monitoring the timing of ice-on and ice-off has been instrumental in estimating the long-term effects of climate change on freshwater lakes and rivers. However, ice thickness has been studied less intensively, both spatially and temporally. Here, we quantified the trends and drivers of ice thickness from 27 lakes and rivers across North America. We found that ice thickness declined on average by 1.2 cm per decade, although ice thickness declined significantly in only four waterbodies. Local winter air temperature, cloud cover, and winter precipitation were the most important determinants of ice thickness, explaining over 81% of the variation in ice thickness. Ice thickness was lower in years and regions with higher air temperatures, high percentage of cloud cover, and high winter precipitation. Our results suggest that warming is contributing to thinning ice, particularly at high latitudes, with potential ramifications to the safety of humans and wildlife populations using freshwater ice for travel and recreation.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".