Investigation of intra-annual glacier velocity and seasonality of Axel Heiberg Island
Bibliographic record
Abstract
The Canadian Arctic Archipelago (CAA) is undergoing rapid atmospheric warming at rates that are twice that of the global average and greater than at any other time in the past four millennia. Longer and more intense melt seasons, modifications in glacier motion, and persistent glacier mass loss are all changes in glacier behavior being experienced due to the increase in air temperature. To understand the impacts of this warming trend, this study will investigate seasonality in ice motion with a focus on two glaciers; Thompson and White Glacier on Axel Heiberg Island, one of which contains the longest in situ mass balance record in the Canadian Arctic. This research builds on previous research by creating a dense time series of glacier motion over a ~10 year period (winter 2008/2009 to winter 2019/2020), thus improving upon spatial and temporal resolution of earlier work. The main objective is to quantify seasonal changes in glacier dynamics within a year using the GAMMA Remote Sensing Software to derive surface velocity from SAR imagery (including RadarSat-2, Terra-SAR-X and Sentinel-1 data). These velocities will help quantify seasonality and determine links between changes in sea ice conditions and summer melt and are important to better characterize differing velocity regimes in the CAA.
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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 | 0.000 |
| 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 | 0.000 | 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".