Evaluating the effectiveness of hazard mapping as climate change adaptation for community planning in degrading permafrost terrain
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it