Global-scale analysis of satellite-derived debris distribution on glacier
Bibliographic record
Abstract
In high relief mountain regions, many glaciers have supraglacial debris in their ablation area, which affects the response of these glaciers to climate change through altering ice melting rates. The thin debris accelerates ice melting and the thick one suppresses it. In order to understand the changes of glacier mass balance and runoff patterns under climate change, it is important to assess the effect of debris-cover on these glaciers. However, the assessment of the debris effect is difficult because it is difficult to measure debris thickness at large scale only from field measurements. Here, we attempted to estimate a global distribution of debris thickness on glaciers by using a thermal resistance of supraglacial debris derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite stereo imageries and radiometer products of Clouds and the Earth’s Radiant Energy System (CERES). The obtained distribution map covers approximately 88% of total glacier area recorded in a global glacier outline of the Randorf Glacier Inventory (RGI). Investigations on several glaciers showed that the ASTER-derived thermal resistances correlated reasonably well with ground-surveyed debris thickness. The results indicate that 11% of total global glaciers are covered by supraglacial debris cover and the regional differences in debris distribution are apparent from region to region. Debris cover is relatively thin and accelerates ice melting in western Himalaya, North America, Canada, and Scandinavia, whereas debris cover is relatively thick and inhibits ice melting in eastern Himalaya, Alps, Caucasus and Andes region.
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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 | 0.001 | 0.001 |
| 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".