Evolution of the dynamics of Airdrop Glacier, western Axel Heiberg Island, over a seven-decade-long advance
Bibliographic record
Abstract
Various remotely sensed data, including historical aerial photographs, declassified intelligence satellite photographs, optical satellite imagery, and synthetic aperture radar data, were used to undertake the first comprehensive reconstruction of the dynamics of Airdrop Glacier on Axel Heiberg Island, Nunavut. Observations show a continuous terminus advance totalling ∼6 km since 1950 and notably less variability in its surface velocities in comparison to adjacent Iceberg Glacier. This advance is concurrent with relatively high flow rates over its entire surface, resulting in significant thickening near the terminus and thinning at higher elevations. Velocities have more than halved from the mid-2000s to 2021, but without any definitive evidence of previous flow instabilities, we cannot confirm whether Airdrop's behaviour is cyclic in nature and therefore characteristic of a surge. Instead, Airdrop Glacier could be experiencing a delayed response to the positive mass balance conditions of the Little Ice Age, which could also explain the advance of other nearby glaciers. Its recent slowdown could then be indicative of a gradual adjustment to recent climatic conditions. This study highlights the need for comprehensive studies of glacier dynamics in the Canadian Arctic to improve our understanding of the factors triggering dynamic instabilities and causing the observed variety of behaviours.
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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 | 0.001 | 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".