Mountain permafrost: development and challenges of a young research field
Bibliographic record
Abstract
Abstract An overview is given of the relatively short history, important issues and primary challenges of research on permafrost in cold mountain regions. The systematic application of diverse approaches and technologies contributes to a rapidly growing knowledge base about the existence, characteristics and evolution in time of perennially frozen ground at high altitudes and on steep slopes. These approaches and technologies include (1) drilling, borehole measurement, geophysical sounding, photogrammetry, laser altimetry, GPS/SAR surveying, and miniature temperature data logging in remote areas that are often difficult to access, (2) laboratory investigations (e.g. rheology and stability of ice-rock mixtures), (3) analyses of digital terrain information, (4) numerical simulations (e.g. subsurface thermal conditions under complex topography) and (5) spatial models (e.g. distribution of permafrost where surface and microclimatic conditions are highly variable spatially). A sound knowledge base and improved understanding of governing processes are urgently needed to deal effectively with the consequences of climate change on the evolution of mountain landscapes and, especially, of steep mountain slope hazards as the stabilizing permafrost warms and degrades. Interactions between glaciers and permafrost in cold mountain regions have so far received comparatively little attention and need more systematic investigation.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".