Will landscape responses reduce glacier sensitivity to climate change in High Mountain Asia?
Bibliographic record
Abstract
In High Mountain Asia (HMA), ongoing climate change threatens mountain water resources as glaciers melt, and the resulting changes in runoff and water availability are likely to have considerable negative impacts on ecological and human systems. Numerous assessments of the ways in which these glaciers will respond to climate warming have been published over the past decade. Many of these assessments have used climate model projections to argue that HMA glaciers will melt significantly this century. However, we show that this is only one way in which these glaciers might respond. An alternative pathway is one in which increasing valley-side instability releases large amounts of rock debris onto glacier surfaces. The development of extensive glacier surface debris cover is common in HMA, and, if thick enough, this surface debris inhibits glacier melting to the extent that glacier ice becomes preserved under the surface debris cover. In so doing, a transition to glacier-derived rock glaciers and other ice debris landforms may prolong the lifetime of HMA glacial ice in the landscape. We call this alternative pathway the Paraglacial Transition Model. In this Perspective Article, we discuss the scientific basis of this alternative view in order to better understand how HMA glaciers may respond to climate change.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".