Facing the challenge of permafrost thaw in Nunavik communities: innovative integrated methodology, lessons learnt, and recommendations to stakeholders
Bibliographic record
Abstract
To support climate change adaptation in the communities of Nunavik, an innovative multitechnique approach to map permafrost conditions and assess risks of geohazards at the community-scale level was applied. Four maps were produced for each community: (1) a surficial geology map, (2) a map of permafrost conditions based on ground-ice content and depth to bedrock, (3) a map of potential for construction, and (4) a geohazard risk assessment map. Local ground temperature data from thermistor cables were used to calibrate 1D numerical models to estimate future permafrost temperature changes and probable rates of degradation in different environmental settings within the communities and under different climate change scenarios for the 2019–2100 period. Throughout this project, abundant consultations were held in communities and with stakeholders to better understand their concerns and to provide pragmatic recommendations for improving construction methods and land-use planning to face the challenges of permafrost thaw. Specific recommendations were made to the higher levels of government for improving construction practices. Inuit aspirations, culture, and leadership remain essential in integrating permafrost geotechnical knowledge in planning a safe future for the communities.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".