Characterizing Arctic sea ice topography using high-resolution IceBridge data
Bibliographic record
Abstract
We present an analysis of Arctic sea ice topography using high resolution, three-dimensional, surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation IceBridge mission. Surface features in the sea ice cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009-2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by ice type to estimate the topographic variability across first-year and multi-year ice regimes. The results demonstrate that Arctic sea ice topography exhibits significant spatial variability, mainly driven by the increased surface feature height and volume (per unit area) of the multi-year ice that dominates the Central Arctic region. The multi-year ice topography exhibits greater interannual variability compared to the first-year ice regimes, which dominates the total ice topography variability across both regions. The ice topography also shows a clear coastal dependency, with the feature height and volume increasing as a function of proximity to the nearest coastline, especially north of Greenland and the Canadian Archipelago. A strong correlation between ice topography and ice thickness (from the IceBridge sea ice product) is found, using a square-root relationship. The results allude to the importance of ice deformation variability in the total sea ice mass balance, and provide crucial information regarding the tail of the ice thickness distribution across the western Arctic. Future research priorities associated with this new dataset are presented and discussed, especially in relation to calculations of atmospheric form drag.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".