Duration of frozen days show a strong decline in the Northern Hemisphere mainly driven by autumn temperature increase
Bibliographic record
Abstract
Thawing permafrost releases methane and carbon dioxide to the atmosphere, contributing to positive feedback loop in global warming. Therefore, accurately monitoring changes in the permafrost freeze–thaw status is imperative. However, the spatiotemporal evolution and potential driving factors remain elusive. Here, we investigated the freeze–thaw status and driving factors by developing novel machine learning models trained on satellite and in situ observations in the Northern Hemisphere. We find that the frozen duration decreased on average by 0.17 days/yr since 1990 with the highest decrease of approximately up to 1.0 days/yr in parts of Belarus and Ukraine, followed by the Yukon region in Canada and Alaska. This decrease is primarily driven by temperatures in boreal autumn and spring and by precipitation and vegetation cover in boreal spring. The frozen duration is projected to decline further with reduction rates doubling until 2050 for the highest and moderate emission scenarios.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".