Evaluating the effectiveness of hazard mapping as climate change adaptation for community planning in degrading permafrost terrain
Bibliographic record
Abstract
Permafrost in northern Canada is susceptible to degradation due to rapid climate change, with hazard mapping promoted as an important activity to guide sustainable community adaptation and planning. This paper presents a framework for evaluating permafrost mapping exercises designed to inform climate change adaptation actions. We apply the framework using a case study of the Incorporating Climate Change into Land Development—Terrain Analysis project (ICCiLD). ICCiLD is a hazard mapping project utilizing interferometric synthetic aperture radar to monitor ground disturbance and categorize land development suitability in seven communities in the territory of Nunavut, Canada. We looked at one of the communities, Arviat, as our case study. We examined technical data and drew upon semi-structured interviews ( n = 19) with map creators and users. We found ICCiLD added new and relevant information for community planning, increased awareness of the risks posed by permafrost thaw and built stakeholder relations. Strong coordination and high public consciousness of local climate impacts emerged as key factors underpinning project success. Nevertheless, in the case of Arviat, the effectiveness of the hazard maps in influencing land-use planning was constrained by communication challenges between project creators and end-users. These challenges included limited community access to the data and uncertainty surrounding how to operationalize the map suitability classifications. Broader climate change adaptation challenges included the presence of other more immediate community planning priorities and a limited ability to incorporate Indigenous ways of knowing into a technical mapping project. The lessons from this evaluation provide insight for the development of mapping-based adaptations across Arctic regions.
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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 | 0.059 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".