Greenland Ice Mapping Project: ice flow velocity variation at sub-monthly to decadal timescales
Bibliographic record
Abstract
We describe several new ice velocity maps produced by the Greenland Ice Mapping Project (GIMP) using Landsat 8 and Copernicus Sentinel 1A/B data. We then focus on several sites where we analyse these data in conjunction with earlier data from this project, which extend back to the year 2000. At Jakobshavn Isbrae and Koge Bugt, we find good agreement when comparing results from different sensors. In a change from recent behaviour, Jakobshavn Isbrae began slowing substantially in 2017, with a mid-summer peak that was even slower than some previous winter minimums. Over the last decade, we identify two major slowdown events at Koge Bugt that coincide with short-term advances of the terminus. We also examined populations of glaciers in northwest and southwest Greenland to produce a record of speedup since 2000. Collectively these glaciers continue to speed up, but there are regional differences in the timing of periods of peak speedup. In addition, we computed trends in winter flow speed for much of the southwest margin of the ice sheet and find little in the way of statistically significant change over the period covered by our data. Finally, although consistency of the data generally is good through time and across sensors, our analysis indicates substantial differences can arise in regions with high strain rates (e.g., shear margins) where sensor resolution can become a factor. For applications such as constraining model inversions, users should factor in the impact that the data's resolution has on their results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".