15-year trends in permafrost, with a focus on community regions
Bibliographic record
Abstract
This deliverable is conducted within the framework of the EU Horizon 2020 project Arctic PASSION, which aims to build a coherent, integrated Arctic observing system to enhance the availability, accessibility and usability of Arctic observations from a diverse set of data providers and knowledge holders. Arctic PASSION’s overarching goal is to support informed decision making and thus sustainable development in the Arctic.This deliverable summarizes the advancements made in Arctic permafrost monitoring in the Arctic PASSION project, with a particular focus on the activities of the Global Terrestrial Network for Permafrost (GTN-P). Permafrost, recognized as one out of four cryosphere Essential Climate Variables (ECV), is critical for understanding climate change impacts on a global scale. GTN-P, as the primary international platform for permafrost ECV data, received sustained support during Arctic PASSION, resulting in strengthened network coordination, improved data infrastructure, and new contributions to international climate guidance, including the WMO’s Best Practices for Permafrost Monitoring.From 2021 to 2025, GTN-P hosted multiple workshops and scientific meetings, notably at EUCOP 2023, the Permafrost DACH Conference 2025, and EGU 2025. These gatherings facilitated expert dialogue on database improvements, standardization, and integration of new variables such as rock glacier velocity. GTN-P’s engagement with early-career researchers and its visibility in international conferences were significantly enhanced through Arctic PASSION funding.Further, the deliverable presents updated permafrost temperature (PT) and active layer thickness (ALT) data from sites across Alaska, Canada, Finland, and Russia for the last 15 years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".