Assessment of permafrost distribution maps in the Hindu Kush Himalayan region using rock glaciers mapped in Google Earth
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Bibliographic record
Abstract
Abstract. The extent and distribution of permafrost in the mountainous parts of the Hindu Kush Himalayan (HKH) region are largely unknown. A long tradition of permafrost research, predominantly on rather gentle relief, exists only on the Tibetan Plateau. Two permafrost maps are available digitally 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 a first-order assessment of these permafrost maps in the HKH region based on the mapping of rock glaciers. Rock glaciers were used as a proxy, because they are visual indicators of permafrost, can occur near the lowermost regional occurrence of permafrost in mountains, and can be delineated based on high-resolution remote sensing imagery freely available on Google Earth. For the mapping, 4000 square samples (~ 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. We use the mapping of rock glaciers in Google Earth 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 does so with more success. Based on this study, the Permafrost Zonation Index is inferred to be a reasonable first-order prediction of permafrost in the HKH. In the central part of the region a considerable deviation exists that needs further investigations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it