Monitoring of temperature-induced deformations in high mountain huts
Bibliographic record
Abstract
Global warming is affecting a lot of human activities and has a relevant environmental impact.This is particularly evident in the Alps, where the recorded temperature increases are larger than average.This trend has direct consequences on the stability of high mountain slopes, as it provokes the upward receding of permafrost and triggers a series of rockfall events.Although it is difficult to pinpoint the direct cause-effect relationship for any recorded event, two general facts emerge: failures are becoming more and more frequent, especially during particularly hot summer seasons; failures tend to concentrate within the altitude range affected by permafrost receding.In this paper, we discuss the structural monitoring at Capanna Margherita hut (Punta Gnifetti 4554m a.s.l., Monte Rosa massif), which is the highest mountain hut in the Alps and Europe in general.Geomechanical monitoring was activated there in 2023 and it is now complemented by the installation of sensors on the hut structure in 2024.A full set of data is continuously being recorded, including inclinometric, thermometric and accelerometric data.The paper analysis is based on the data recorded by the sensors and the air temperature at Capanna Margherita hut, including the relationship between the temperatures and displacements, and the dynamic characteristics of structural vibrations.
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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) | 0.001 | 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".