Modern landscape change in High Mountain Asia (1950-present)
Bibliographic record
Abstract
High Mountain Asia (HMA) – the Tibetan Plateau and surrounding high Asian mountains – is now experiencing amplified climate change, glacier melt, and permafrost thaw. The rapid climate change and melting and thawing of the cryosphere are not only affecting the water cycle but also causing landscape instability and mountain hazards, potentially threatening over 2 billion people in the downstream river basins. Glacier retreat and permafrost thaw are accelerating associated with frequent rockfalls, landslides, and debris flows. Lake outburst floods from (pro)glacial- and landslide-dammed lakes have potential runout distances of hundreds of kilometers. Moreover, greater amounts of sediment are mobilized, and fluvial sediment fluxes are increasing. Such mountain landscape instability can be largely attributed to climate change and is threatening infrastructure and livelihoods. We suggest that policymakers and stakeholders in the Himalaya countries must be urgently and fundamentally aware of these increasing threats in a changing climate. Adaptation measures should be based on extensive and continual monitoring of the glaciers, permafrost, unstable paraglacial landscapes, and sediment transport, to better understand compound and cascading hazards.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".