Fluctuations of Raikot Glacier during the past 70 years: a case study from the Nanga Parbat massif, northern Pakistan
Bibliographic record
Abstract
Abstract The Himalaya has some of the largest glacier concentrations outside the polar regions. Despite this, long-term measurements detecting the impact of global warming and changing precipitation patterns on glaciers are rare. The Nanga Parbat massif in northern Pakistan is an exception. The cartographer and glaciologist R. Finsterwalder investigated glacier dynamics of this mountain massif in the 1930s, and several other studies document changes since then. The aim of this study is to detect and analyse the changes of Raikot Glacier over the past seven decades. We use a multitemporal and multiscale approach, based on repeat terrestrial images, additional historical data and remotely sensed imagery (Corona, ASTER, Landsat, QuickBird). The multitemporal approach covers the period 1934–2007. While the analyses show a total glacier retreat of ~200 m in 73 years, this general trend was interrupted by a significant glacier advance between the 1950s and 1980s. Although down-wasting processes can be inferred from an increase in debris-covered area, a general trend of reduced glacier thickness does not appear significant over the whole observation period.
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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 | 0.001 | 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".