Alpine glaciers disappearance tipping point: results from EURO-CORDEX models
Bibliographic record
Abstract
The front variations of Alpine glaciers show a general retreat over the past 150 years. This glacier retreat, then, has a large impact on many regional sectors, such as hydroelectricity production, river runoff, and touristic sector. In the last decades, glacier retreat in the Alps has been extremely evident due to the pronounced temperature increase affecting these mountains. Moreover, numerous model studies exhibit a high probability of occurrence of Alpine glacier disappearance by the end of the current century, especially under extreme future climate change conditions. The Alpine glaciers disappearance is expected to largely influence the Alpine glaciers regions climate, especially in terms of water availability. For this reason, the occurrence of the Alpine glaciers disappearance is enumerated among the climate tipping point. Given the reduced average glaciers dimension, high-resolution data are needed to investigate the occurrence and the potential impacts of this tipping point. Thus, the EURO-CORDEX dataset over the EUR-11 domain are analyzed in this study. Alpine glaciers differ under many characteristics, such as elevation, mean aspect, length, and shape. Consequently, a minimal glacier model, which takes into account few glacier features, is used in detecting the occurrence of the Alpine glaciers disappearance. Besides, a simplified surface mass balance model contributes to generalize the tipping point detection and assess the expected water budget changes. This effort is part of the EU-funded COACCH project. Keywords Alpine Glaciers, tipping point, water cycle, EURO-CORDEX
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".