Drift, drag and deterioration: mapping the fate of large ice hazards
Bibliographic record
Abstract
Floating ice hazards are a risk to safe navigation in both the Arctic and the Southern Ocean as a result of changing ice conditions and increased marine traffic in recent years. These hazards include icebergs and ice islands (a type of iceberg in the Arctic that is tabular in shape and up to several km in length). Under the influence of climate change, calving (break-off) rates of tidewater glaciers, floating glacier tongues and ice shelves, the source of icebergs and ice islands, appear to be increasing, particularly in the Arctic. Understanding the drift and deterioration of these ice features is a key challenge to the ice community and tends to be limited by a paucity of observational data from around Newfoundland and Labrador, where water temperatures are significantly warmer and interaction with sea ice is less common than in Arctic waters. Observations of drifting ice islands that calved from the Petermann Glacier in NW Greenland were attempted in 2011 in the Canadian High Arctic (69-75N) to examine draft, surface roughness and basal features using an Autonomous Underwater Vehicle (AUV) to help improve numerical ice hazard drift models. The AUV was successfully deployed under a grounded ice island, yet key challenges for future deployments involve mapping ice that is both drifting and rotating. Acoustic localization, combined with terrain-relative navigation, is proposed to deal with this motion, enabling accurate in situ measurements of the underside and sidewalls of the ice. This data is required to help understand the drift, deterioration and ultimate fate of these ice hazards in a changing global climate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".