Bibliographic record
Abstract
Abstract. Tuktoyaktuk Island acts as a natural breakwater, protecting the harbour and townsite of Tuktoyaktuk — an Arctic community that has faced coastal retreat and its consequences for decades. Increasing storm activity, coupled with a longer open-water season, is rapidly eroding the island's shoreline and inundating the underlying permafrost. Once inundated, permafrost warms and degrades, further undermining coastal stability. This study investigates both short and long-term permafrost changes during the transition from terrestrial to subsea. We used Electrical Resistivity Tomography (ERT) to estimate the depth of the ice-bearing subsea permafrost table (IBPT), capturing the short-term response. By integrating subsurface resistivity data with historical shoreline positions and thermal modelling, we also gain insights into long-term degradation patterns. Our results reveal a distinct contrast in IBPT shape between the ocean-facing and harbour-facing nearshore zones, indicating the influence of coastal erosion rates and corresponding inundation times. Additionally, small-scale variations appear linked to local geological differences. In the long term, changes in subsurface composition point to more rapid ice loss within the permafrost than can be explained by the temperature gradient caused by inundation alone. We suggest that subsea permafrost north of the island is more degraded than previously thought, potentially accelerating the projected breach, which was last estimated to occur by 2044. These findings enhance our understanding of subsurface processes driven by coastal retreat and offer valuable insights that can inform engineering strategies to fortify the island.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.021 | 0.010 |
| Insufficient payload (model declined to judge) | 0.393 | 0.270 |
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".