Arctic permafrost landscapes in transition: towards an integrated Earth system approach
Bibliographic record
Abstract
Permafrost science and engineering are of vital importance for northern development and climate adaptation given that buildings, roads, and other infrastructure in many parts of the Arctic depend on permafrost stability. Permafrost also has wide-ranging effects on other features of the Arctic environment including geomorphology, biogeochemical fluxes, tundra plant and animal ecology, and the functioning of lake, river, and coastal marine ecosystems. This review presents an Earth system perspective on permafrost landscapes as an approach towards integration across disciplines. The permafrost system can be described by a three-layer conceptual model, with an upper buffer layer that contains vegetation or infrastructure. Snow and liquid water strongly affect the thermal properties and stability of these layers and their associated interfaces, resulting in critical times and places for accelerated degradation of permafrost and for exchanges of mass and heat with the hydrosphere and atmosphere. Northern permafrost landscapes are now in rapid transition as a result of climate warming and socioeconomic development, which is affecting their ability to provide geosystem and ecosystem services. The Earth system approach provides a framework for identifying linkages, thresholds, and feedbacks among system components, including human systems, and for the development of management strategies to cope with permafrost 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".