Permafrost thermal regime and active layer dynamics across periglacial landforms in Svalbard's Nordenskiöld Land Permafrost Observatory, 2008–2023
Bibliographic record
Abstract
On Spitsbergen, Svalbard, the Nordenskiöld Land Permafrost Observatory provides ground temperature time series from 2008 to the present in 16 boreholes located in a variety of periglacial landforms. This study presents trends in permafrost temperatures and active layer thickness, compares these trends to observed climatic changes, and differentiates the climate sensitivity of the studied periglacial landforms. Ground temperature variability in these landforms is driven by Svalbard's air temperature gradients due to elevation and from the warmer west coast to the colder interior, in addition to snow cover and landform dynamics. During the study period, increases in permafrost temperatures and active layer thickness, closely tied to rapid climate warming on Svalbard, were observed at nearly all sites. The observed rates of active layer thickness increase, ranging from 0.5 to 10.7 cm/year, are on the high end of observed values across the circum-Arctic. Decadal increase in temperature at 20 m depth reaches 0.9 °C; the Canadian High Arctic and the Beaufort–Chukchi region are the only Arctic areas with permafrost warming of comparable magnitude. The landforms that are entirely or predominately composed of bedrock or a blocky substrate are the most thermally sensitive to climate change.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".