Pan-Arctic Exposure of Buildings to Permafrost Degradation Is Higher than Previously Estimated
Bibliographic record
Abstract
Permafrost degradation linked to rapidly rising air temperatures across the Arctic drives geologic hazards, such as weakened ground bearing capacity and differential ground subsidence. This threatens to disrupt the lives of millions of people living in the Arctic by damaging infrastructure needed to sustain over 1000 high-latitude communities on permafrost. Publicly-available geospatial data, such as OpenStreetMap, used to estimate exposure of infrastructure to hazardous permafrost degradation is limited in geographic coverage across the pan-Arctic. This has led to inaccuracies in the estimated spatial distribution of risk. Using a deep learning model trained to detect buildings from sub-meter resolution Maxar satellite imagery of Arctic communities, we mapped a building area of 52 million m 2 (454,947 individual buildings) missing from the OpenStreetMap dataset, expanding the mapped area of buildings by 22% at the pan-Arctic scale. Building area was expanded by 45% in Alaska, 23% in Russia, and 13% in Canada. The highest total area of new buildings was mapped in Russia at 42 million m 2 (384,850 buildings). Combining OpenStreetMap with this newly mapped building area suggests that 64% and 52% more pan-Arctic building area is exposed to highly-hazardous permafrost degradation by 2041-2060 under the RCP4.5 and RCP8.5 scenarios, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".