Firn Pack Changes on White Glacier, Axel Heiberg Island, Nunavut
Bibliographic record
Abstract
The near-surface processes and variability within the firn pack of Arctic glaciers are a significant source of uncertainty in estimating future glacier responses to climate warming. This study provides the first characterization of the firn pack of White Glacier, Axel Heiberg Island, Nunavut, and an analysis of recent firn pack changes (2013-2019). Utilizing ground penetrating radar (GPR) surveys the firn pack thickness, extent, and associated topographic controls on firn distribution were determined. Two methods of GPR analysis were tested in this study. The first followed the traditional approach of conducting visual interpretation of radargrams to identify zones of backscatter associated with firn. The second is a proposed new methodology that uses average backscatter values from each radar return as a proxy indicator of firn presence in the subsurface. The results of these two approaches showed that the firn pack on White Glacier has reduced in extent, and reductions in average backscatter values suggest that the density of the firn has increased in the near surface. Overall, the long-term firn area decreased in extent by 3.96 km2 (10% of the total glacier area) between 2013 and 2018. Rates of surface lowering were determined using dual-frequency GPS surveys. For spring 2018 to spring 2019 the rate was -0.165 ± 0.29 m a-1 in the accumulation area, likely driven by the near surface densification. The potential for average backscatter values to provide information about near surface snow water equivalence is also explored.
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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 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".