Controls on Seasonal and Multi-Year Velocity Variability of South Croker Bay Glacier, Nunavut, Canada from 2015-2020
Bibliographic record
Abstract
Velocity records of South Croker Bay Glacier (Devon Ice Cap, Canadian Arctic) obtained from offset tracking of 11-day separated TerraSAR-X image pairs from 2015 to 2020 have captured a significant increase in both ‘winter’ (September-May) and ‘summer’ (June-August) seasons. Winter velocities have increased from 179 m a-1 in 2015 to 251 m a-1 in 2020, with the most significant change identified in 2016/17 increasing from 172 m a-1 to 239 m a-1 in 2018/19. Summer velocities have been following the same upward trend, with velocities of 299 m a-1 observed in 2015, increasing to 397 m a-1 in 2021. The highest velocities are found ~4.5 km up-glacier from the terminus where the bed lies ~50-100 m below sea level. 3D Tomography data from NASA’s Operation IceBridge is used to investigate basal topography as a spatial control on the propagation of faster glacier speeds. Supraglacial lakes are manually delineated from cloud free optical imagery (Sentinel-2 and Landsat-8/9) and are tracked based on their evolution and drainage to help determine the supra-glacial hydrology structure as well as identify when surface water drains to the bed and impacts glacier sliding rates. Sea ice concentrations, as determined from Canadian Ice Service charts at the front of South Croker Bay Glaciers, are used to determine how the observed flow rates of the glacier are linked to changing sea ice conditions. Finally, terminus positions are digitized and measured to assess how the front of the glacier has responded to the variability in ice dynamics over the observation period. Collectively, this work provides one of the most comprehensive records of motion for any glacier in the Canadian High Arctic and allows us to explore how bed topography, sea ice conditions and surface hydrology cause and control variations in flow speeds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".