Seasonal and Multi-annual Dynamic Monitoring of Belcher Glacier, Nunavut Canada using SAR Imagery
Bibliographic record
Abstract
Belcher Glacier is one of the largest tidewater outlets glaciers of Devon Ice Cap, and a key area of ice discharge to the ocean within the Canadian Arctic as a whole. The glacier covers an area of 1180 km2, and ranges in elevation from sea level to around 1920 m. Previous work has mapped the motion of Belcher Glacier at annual timesteps using Synthetic Aperture Radar (SAR) imagery (primarily Radarsat-2) and has found that the terminus section of the glacier has sped up in recent years. This observation has led to speculation that the glacier may be experiencing acceleration driven by thinning of the terminus region in response to warming Arctic air temperatures. The goal is to investigate the dynamics of Belcher Glacier and the processes that are driving its acceleration. Therefore, we are using a high-temporal time series from April 2022 to present acquired with TerraSAR-X Stripmap data with a repeat orbit of 11 days. We augment this record with results generated from imagery collected by the Radarsat Constellation Mission. The timeseries allows us to investigate variations in glacier flow at an unprecedent spatial and temporal resolution. This work will present the preliminary seasonal dynamics of Belcher Glacier over this time period and provide an intercomparison between glacier velocity results generated from different sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.017 | 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".