Kinematic observations of the mountain cryosphere using in-situ GNSS instruments
Bibliographic record
Abstract
Abstract. Permafrost warming is coinciding with accelerated mass movements, talking place especially in steep, mountainous topography. While this observation is backed up by evidence and analysis of both remote sensing as well as repeat terrestrial surveys undertaken since decades much knowledge is to be gained about the specific details, the variability and the processes governing these mass movements in the mountain cryosphere. This dataset collates data of continuously acquired kinematic observations obtained through in-situ Global Navigation Satellite Systems (GNSS) instruments that have been designed and implemented in a large-scale multi field-site monitoring campaign across the whole Swiss Alps. The landforms covered include rock glaciers, high-alpine steep bedrock bedrock as well as landslide sites, most of which are situated in permafrost areas. The dataset was acquired at 54 different stations situated at locations from 2304 to 4003 m a.s.l and comprises 209’948 daily positions derived through double-differential GNSS post-processing. Apart from these, the dataset contains down-sampled and cleaned time series of weather station and inclinometer data as well as the full set of GNSS observables in RINEX format. Furthermore the dataset is accompanied by tools for processing and data management in order to facilitate reuse, open alternate usage opportunities and support the life-long living data process with updates. To date this dataset has seen numerous use cases in research as well as natural-hazard mitigation and adaptation due to climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".