Local risks from Arctic permafrost thaw – Results from a transdisciplinary, comparative analysis
Bibliographic record
Abstract
This dataset underpins the findings of a transdisciplinary and comparative assessment of permafrost thaw risks across four distinct Arctic regions: Longyearbyen (Svalbard, Norway), the Avannaata Municipality (Greenland), the Beaufort Sea region and Mackenzie River Delta (Canada), and the Bulunskiy District of the Sakha Republic (Yakutiya, Russia). Information on permafrost thaw risks was gathered from multiple disciplines and stakeholders over a five-year period from 2019 to 2023, and classified via thematic network analysis (Attride-Stirling, 2001). The identified risks were subsequently verified and ranked by scientists and local experts through an iterative process and a series of workshops (see Ingeman-Nielsen et al., 2024 in Related Works). The dataset contains the results of the thematic network analysis and ranking of permafrost thaw risks specific to each Arctic region. Provided as an .xlsx file, it consists of six main sheets comprising the following information: Global theme - Physical Processes Ranking - Physical Processes Global theme - Key Hazards Global theme - Societal Consequences Ranking - Consequences List of Actions Needed Additional details about the methodological approach and dataset can be found in the accompanying README document.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".