Thermal and Mechanical Modeling of Coastal Erosion Processes on Tuktoyaktuk Island, Northwest Territories
Bibliographic record
Abstract
Arctic coasts are particularly vulnerable to rapid and extreme erosion due to the presence of ice-rich permafrost sediment, with erosion rates varying anywhere from 1 to 20 m/yr in the region. Erosion is limited to the open-water season such that the factors controlling rates of erosion are warmer air temperatures and storm surges impacting the sensitive ice-rich permafrost coastal bluffs. Erosional processes in the Arctic are unique and consist of coupled thermal and mechanical mechanisms. The coastal community of Tuktoyaktuk, Northwest Territories, located along the Beaufort Sea coast in the western Canadian Arctic, has been dealing with the consequences of coastal erosion for many decades and will likely face displacement due to accelerating rates of erosion. In this study, a process-based thermal-mechanical erosion numerical model was developed for Tuktoyaktuk Island, which currently shelters the harbour and eastern shores of the community from wave impact, to investigate erosional processes commonly impacting ice-rich permafrost coasts including thermal denudation of the cliff face, and thermal abrasion and formation of thermoerosional niche at the base of the cliff under a storm surge to understand the impact of permafrost sediment properties on rates of erosion. It was found that erosion rates vary significantly between stratigraphic units, where sandy silt sediments have higher rates than ice rich clayey silt layers due to latent heat effects, and therefore should be considered on a site-specific scale for engineering purposes rather than the traditional cliff edge retreat method. The increased granularity improved our understanding of erosion rates on Tuktoyaktuk Island thus enabling future detailed consideration of mitigation strategies. It was concluded that massive block failure due to the formation of a thermoerosional niche under a storm surge is presently unlikely to occur on the island. For block failure to occur, either a storm of extreme duration or storm surge level is required. Lastly, it is expected that erosion rates will increase under climate-driven change such that the drivers of accelerated erosion are relative sea level rise, decrease in sea ice extent, and increase in surface air temperatures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".