Remote-sensing assessment of glacier fluctuations in the Hindu Raj, Pakistan
Bibliographic record
Abstract
Space-based assessments of glaciers across the Himalayas indicate that there is a spatial variation in glacier fluctuations due to variations in local topography, regional climate, and ice-flow dynamics. Unfortunately, limited information is available on glacier fluctuations in northern Pakistan. In this work, we quantify the glacier terminus variations in the Hindu Raj region of Pakistan, where we used Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) time-series data for 1972, 1989, 1999, and 2007. Eighty-five mountain glaciers of various sizes, orientations, and altitudes were sampled. Our results show that most of the glaciers (70.6%) retreated over the last four decades, although some glaciers advanced (17.6%) or exhibited no detectable change in terminus position (11.8%). Larger glaciers with lower terminus altitudes exhibited greater retreat distances than smaller high-altitude glaciers. Long-term climate data analysis reveals that the recession of glaciers appears to be associated with the rising of summer temperatures in the Hindu Raj. Our results support a spatial trend of an increase in shrinking glaciers towards the western portion of northern Pakistan, with a greater frequency of advancing glaciers towards the east.
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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) | 0.000 | 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 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".