Potential Cost Impacts for Adaptation of Building Foundations in the Northwest Territories
Bibliographic record
Abstract
The Northwest Territories (NWT) is within the zone of permafrost. Many buildings, particularly in the northern NWT, are founded on permafrost. The last 30 years have exhibited increased warming trends. Continued warming will result in increased ground temperatures and thaw of permafrost, in turn resulting in ground settlement and increased seasonal freeze-thaw effects. For buildings founded on or in permafrost, this can be expected to bring about foundation distress. This paper describes the development of cost estimates to adapt existing building foundation infrastructure in the NWT to climate change impacts. Building foundation inventories were compiled for six communities in the NWT. The communities were selected to represent a range of sensitivities, geographic regions and populations. Unit costs to adapt different foundation types were developed, based on building size. The unit costs were applied to the inventories to determine a potential cost to adapt the foundations in each community. The findings from each community were upscaled to the entire NWT by considering community size and estimated distributions of foundation type. The estimated "worst case" cost, assuming all foundations on permafrost would require adaptation, totals about $420 million. A "best guess" adaptation cost, considering the thermal and physical sensitivity of each community, is in the range of $200 to $250 million.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".