Seasonality of Belcher and South Croker Bay Glaciers from 2013 to 2021
Bibliographic record
Abstract
This study investigates the seasonality of surface ice motion of Belcher and South Croker Bay Glaciers, two of the fastest flowing and highest discharging glaciers on Devon Ice Cap, Nunavut, Canada, from 2009 to 2021. We utilize a variety of remote sensing datasets (RADARSAT-2, TerraSAR-X imagery, ITS-Live) to map ice motion at 11-day to monthly time-scales, creating a catalogue of motion that can be used to specifically quantify the seasonality of these glaciers. Potential drivers of the observed seasonality will be investigated, including sea ice concentrations at the terminus that can provide buttressing for the terminus and modify terminus flow rates; meltwater production and lake drainage that can contribute to basal sliding which increases surface velocities; and bed topography that can control how far up-glacier seasonality is observed. This study contributes to the overall understanding of how glacier motion may be impacted in a warming climate.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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; both teacher heads agree on what is shown here.
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".