Assessment of Correlation between Sea Ice Fractures and Meteorological Conditions in Tuktoyaktuk
Bibliographic record
Abstract
Abstract. Landfast sea ice is a crucial component of the Arctic ecosystem and holds significant cultural importance for indigenous communities in the Canadian Arctic. They rely on it for hunting, navigation, and maintaining connections between communities. As a climate indicator, landfast sea ice plays a key role in heat exchange between the ocean and the atmosphere, serves as a buffer against storms and coastal erosion, and impacts local environmental stability. However, the Arctic is experiencing rapid warming, which is leading to a reduction in the extent, thickness, and strength of sea ice. This trend increases the vulnerability of communities dependent on landfast ice to severe environmental events. This study focuses on the community of Tuktoyaktuk in the Northwest Territories, Canada, where landfast sea ice has been observed to fracture more frequently in the winter season. Using satellite-based Synthetic Aperture Radar (SAR) and meteorological data, this research identifies that changing wind directions, particularly strong western and north-western winds, are significant factors contributing to the displacement of ice from the shore. Observations reveal a growing frequency of ice fracture events from 3 to 13 fracture counts from 2016–2017 to 2022–2023, coinciding with an increase in the occurrence of strong winds along the Tuktoyaktuk coast. The study aims to assess the likelihood of landfast sea ice fracturing under specific wind conditions, providing insights into the impact of climatic changes on the stability of ice in this region.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".