Spatio-temporal variability of snow accumulation on the Biafo and Hispar glaciers in the central Karakoram
Bibliographic record
Abstract
The Karakoram is an extensively glacierised mountain range in the western part of High Mountain Asia and constitutes an important source of fresh water for millions of people in the Indus Basin. Over the last years, the Karakoram has attracted increasing attention due to an anomalous glacier stability, which contrasts the progressing ice mass loss across the Himalaya. Decreasing summer temperatures and increasing winter precipitation have been proposed as potential causes for the anomaly. However, the lack of snow accumulation studies and long-term meteorological measurements above 3,000 m a.s.l. hampers the corroboration of this hypothesis. To quantify the spatial and temporal variability of snow accumulation in the central Karakoram, we followed the track of a Canadian research expedition from 1986. We reinvestigated eight sites between ca. 4,400 and 5,200 m a.s.l. in the connected accumulation zone of the Biafo and Hispar glaciers in 2019. Density measurements were performed in each snow pit down to the summer horizon of the previous year to quantify the elevation-dependent amount of annually accumulated snow. In addition, snow samples were collected from three selected pits for the analysis of rare earth elements and stable water isotopes to constrain the origin and seasonality of the deposited snow. Finally, we compared our recent measurements with the 30-year-old results from the Canadian research expedition as well as independent meteorological data. In doing so we aim to evaluate the hypothesised increase in winter precipitation in this 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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".