Identifying increasing risks of hazards for northern land-users caused by permafrost thaw: integrating scientific and community-based research approaches
Bibliographic record
Abstract
Abstract Understanding the causes and consequences of environmental change is one of the key challenges facing researchers today as both types of information are required for decision making and adaptation planning. This need is particularly poignant in high latitude regions where permafrost thaw is causing widespread changes to local environments and the land-users who must adapt to changing conditions to sustain their livelihoods. The inextricable link between humans and their environments is recognized through socio-ecological systems research, yet many of these approaches employ top-down solutions that can lead to local irrelevance and create tensions amongst groups. We present and employ a framework for the use both of scientific and community-based knowledge sources that provides an enriched and thematic understanding of how permafrost thaw will affect northern land-users. Using geospatial modeling of permafrost vulnerability with community-based data from nine rural communities in Alaska, we show that permafrost thaw is a major driver of hazards for land-users and accounts for one-third to half of the hazards reported by community participants. This study develops an integrated permafrost-land-user system, providing a framework for thematic inquiry for future studies that will add value to large-scale institutional efforts and locally relevant observations of environmental 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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".