Investigation of intra-annual glacier velocity and seasonality of White and Thompson Glaciers, Axel Heiberg Island, Nunavut
Bibliographic record
Abstract
The Canadian Arctic Archipelago (CAA) is undergoing rapid atmospheric warming at rates that are twice that of the global average and are greater than any other time in the past four millennia. Consequently, changes in glacier behavior are being experienced due to this increase in air temperature, including longer and more intense melt seasons, modifications in glacier motion, and persistent glacier mass loss. To further understand the impacts of this warming trend on glacier flow, this study investigates seasonality and long-term changes in ice motion with a focus on two glaciers: Thompson and White Glaciers on Axel Heiberg Island, with White Glacier containing the longest in situ mass balance record in the Canadian Arctic. This study builds on previous research by creating a dense time series of glacier motion over a ~10-year period (winter 2008/2009 to winter 2021/2022), thus improving upon spatial and temporal resolution of earlier work. The main objectives of this study are to (1) utilize a large catalogue of previously unused SAR (R2 and TSX) data to produce velocity maps of White and Thompson Glaciers, (2) perform a comparison of different SAR datasets and (3) investigate seasonality and long-term changes in velocity structure.
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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 | 0.002 |
| 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) | 0.000 | 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".