Extracting recent short-term glacier velocity evolution over southern Alaska and the Yukon from a large collection of Landsat data
Bibliographic record
Abstract
The measurement of glacier velocity fields using repeat satellite imagery has become a standard method of cryospheric research. However, the reliable discovery of important glacier velocity variations on a large scale is still problematic because time series span different time intervals and are partly populated with erroneous velocity estimates. In this study we build upon existing glacier velocity products from the GoLIVE dataset (https://nsidc.org/data/golive, last access: 26 February 2019) and compile a multi-temporal stack of velocity data over the Saint Elias Mountains and vicinity. Each layer has a time separation of 32 days, making it possible to observe details such as within-season velocity change over an area of roughly 150 000 km2. Our methodology is robust as it is based upon a fuzzy voting scheme applied in a discrete parameter space and thus is able to filter multiple outliers. The multi-temporal data stack is then smoothed to facilitate interpretation. This results in a spatiotemporal dataset in which one can identify short-term glacier dynamics on a regional scale. The goal is not to improve accuracy or precision but to enhance extraction of the timing and location of ice flow events such as glacier surges. Our implementation is fully automatic and the approach is independent of geographical area or satellite system used. We demonstrate this automatic method on a large glacier area in Alaska and Canada. Within the Saint Elias and Kluane mountain ranges, several surges and their propagation characteristics are identified and tracked through time, as well as more complicated dynamics in the Wrangell Mountains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".