Permafrost-Related Geohazards and Infrastructure Construction in Mountainous Environments
Bibliographic record
Abstract
Abstract Mountain environments, home to about 12% of the global population and covering nearly a quarter of the global land surface, create hazardous conditions for various infrastructures. The economic and ecologic importance of these environments for tourism, transportation, hydropower generation, or natural resource extraction requires that direct and indirect interactions between infrastructures and geohazards be evaluated. Construction of infrastructure in mountain permafrost environments can change the ground thermal regime, affect gravity-driven processes, impact the strength of ice-rich foundations, or result in permafrost aggradation via natural convection. The severity of impact, and whether permafrost will degrade or aggrade in response to the construction, is a function of numerous parameters including climate change, which needs to be considered when evaluating the changes in existing or formation of new geohazards. The main challenge relates to the uncertainties associated with the projections of medium- (decadal) and long-term (century-scale) climate change. A fundamental understanding of the various processes at play and a good knowledge of the foundation conditions is required to ascertain that infrastructure in permafrost environment functions as intended. Many of the tools required for identifying geohazards in the periglacial and appropriate risk management strategies are already available.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".