Present and future states of Himalaya and Karakoram glaciers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A complete glacier inventory of the Himalaya and Karakoram (H-K) has been created by merging records from the Chinese Glacier Inventory, several regional inventories produced by the International Centre for Integrated Mountain Development, Kathmandu, Nepal, and partial inventories from the Geological Survey of India. The only remaining gap, the Indian part of Kashmir, has been filled by a reconnaissance inventory based on Soviet military maps at 1 : 200 000 scale representing the late 1970s. It contains records for 3526 glaciers covering 9584 km 2 . The new H-K inventory contains records and outlines for 20 812 glaciers covering 43 178 km 2 . The extent of ice in the Karakoram is slightly less than in the Himalaya, but the Karakoram glaciers are on average twice as thick (~160m as against ~80 m). A glacier-by-glacier analysis, relying on estimates of mass balance for the entire mountain range and on an extension of the often-used volume–area scaling relation, suggests that up to about one-fifth of the glaciers present in 1985 may have disappeared already. If mass loss were to remain constant at the average rate for 1975–2008, from 3000 to 13 000 more glaciers might disappear by 2035. If mass loss were to continue to accelerate as inferred for 1985–2008, only a few thousand to a few hundred glaciers might remain in 2035. Total area and total mass would each decrease by about one-half (constant-rate assumption) or three-quarters (constant-trend assumption). These projections, which are uncertain and neglect some possibly important mitigating controls, such as variable extents of debris cover and the feedback due to retreat to higher elevations, demonstrate the need for more complete analyses to inform public perceptions of, and policy decisions relating to, the health of H-K glaciers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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