Ice velocity and climate variations for Baltoro Glacier, Pakistan
Bibliographic record
Abstract
Abstract The recent dynamic behaviour of Karakoram glaciers is expected to differ from that shown by glaciers in the central and eastern Himalaya because of regional variations in precipitation and temperature trends. However, there are insufficient quantitative data to support or confute such hypotheses. We present velocity data covering the period 1993–2008 for Baltoro Glacier, one of the longest glaciers in the Karakoram. Velocity measurements were made using cross-correlation feature tracking applied to European Remote-sensing Satellite (ERS-1 and -2) and Envisat advanced synthetic aperture radar (ASAR) data, supplemented by differential global positioning system (DGPS) measurements. We find a gradual acceleration of the glacier during the early 2000s, in particular during winter months. Multi-seasonal data reveal a large difference between summer and winter flow characteristics, but only in the upper ablation zone. Summer 2005 was a particularly dynamic period following from the heavy winter snowfall of 2004, indicating the importance of basal meltwater availability for glacier flow. Transverse velocity profiles indicate that Baltoro Glacier undergoes ‘block’ flow across much of the upper ablation zone during the summer, which we interpret as evidence of widespread basal sliding. The DGPS data confirm the rapid increase in flow detected during 2005. Modelled climatic data reveal decreasing summer temperatures and increasing precipitation over the study period, helping to explain the observed dynamic variations and their differences from glaciers elsewhere in the Himalaya.
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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) | 0.001 | 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".