Seasonal Variability of Ice Motion for Hubbard and Valerie Glaciers, Alaska
Bibliographic record
Abstract
Hubbard Glacier is a large fast-flowing tidewater-terminating glacier in the St. Elias Mountains and is connected at its terminus to Valerie Glacier. Although Hubbard Glacier has been shown to experience large intra-annual velocity changes and a long-term deceleration, previous seasonality studies have had limited timescale without a dense record of motion. Valerie Glacier’s variability has also been understudied, with only one study reporting its seasonal behaviour. The goal of this study was to combine ITS_LIVE, RADARSAT-2, RADARSAT Constellation Mission, and TerraSAR-X/TanDEM-X derived velocity data to create the densest record of motion ever constructed for Hubbard and Valerie glaciers from July 2013-April 2022 in order to explore seasonal velocity variability of both glaciers. Air temperature (NCEP-NCAR Reanalysis) was used to estimate surface melt on the glaciers, which was explored as a potential driver for seasonal velocity changes. Valerie Glacier had a seasonal pattern of fast flow in May, with minimum flow between August-November before accelerating again. Hubbard Glacier displayed a unique seasonal pattern that has not been previously observed on this glacier, with two periods of fast motion: one in May and one in December-February. It is inferred that the spring peaks and late summer/fall minimums on both glaciers are due to meltwater reaching the glacier bed and influencing the subglacial hydrology. The cause of the winter peak and slight velocity drop before the spring peak on Hubbard Glacier has not been determined and should be a topic for future studies, although it is hypothesized to influenced by its geometry.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".