Assessment of permafrost distribution maps in the Hindu Kush–Himalayan region using rock glaciers mapped in Google Earth
Bibliographic record
Abstract
Abstract. The extent and distribution of permafrost in the mountainous parts of the Hindu Kush–Himalayan (HKH) region have barely been investigated and are largely unknown. Only on the Tibetan Plateau a long tradition of permafrost research on rather gentle relief exists. Two permafrost maps are available that cover the HKH and provide estimates of permafrost extent, i.e. the areal proportion of permafrost: the manually delineated Circum-Arctic Map of Permafrost and Ground Ice Conditions (Brown et al., 1998) and the Global Permafrost Zonation Index, based on a computer model (Gruber, 2012). This article provides first-order assessment of permafrost maps of the HKH region based on the mapping of rock glaciers. Rock glaciers were used as a proxy, because they are visual indicators of permafrost, often occurring near the lowermost regional occurrence of permafrost in mountains, and because they can be delineated based on high-resolution remote sensing imagery freely available on Google Earth. For the mapping 4000 square samples (approx. 30 km2) were randomly distributed over the HKH region. Every sample was investigated and rock glaciers were mapped by two independent researchers following precise mapping instructions. Samples with insufficient image quality were recorded but not mapped. It is shown that mapping of rock glaciers in Google Earth can be used as first-order evidence for permafrost in mountain areas with severely limited ground truth. The minimum elevation of rock glaciers varies between 3500 and 5500 m a.s.l. within the region. The Circum-Arctic Map of Permafrost and Ground Ice Conditions does not reproduce mapped conditions in the HKH region adequately, whereas the Global Permafrost Zonation Index appears to be a reasonable first-order prediction of permafrost in the HKH. Only in the central part of the region a considerable deviation exists that needs further investigations.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".