Revised Estimates of Recent Mass Loss Rates for Penny Ice Cap, Baffin Island, Based on 2005–2014 Elevation Changes Modified for Firn Densification
Bibliographic record
Abstract
Abstract Repeat airborne or satellite measurements of surface elevation over ice caps are often used to calculate glacier‐wide surface mass changes over time. However, these measurements typically do not account for vertical ice motion caused by firn densification and/or ice flow, so the effect of these factors for mass change measurements over an entire ice cap are currently poorly constrained. In this study, we update NASA Airborne Topographic Mapper (ATM) altimetry elevation changes across Penny Ice Cap (Baffin Island, Canada) to assess total changes in ice mass from 2005–2014, relative to 1995–2000. Dual‐frequency GPS measurements and temporal changes in ice core density profiles are used to calculate firn densification and ice flow to isolate the component of elevation change due to surface mass change. Envisat satellite imagery is used to delineate the areas impacted by firn densification. These calculations, the first for a Canadian Arctic ice cap, indicate that accounting for firn densification may reduce the inferred surface mass loss by ~13–15%. Overall, there has been a fourfold increase in mass loss from Penny Ice Cap between 1995–2000 (−1.3 ± 0.7 Gt a−1) and 2005–2013 (−5.4 ± 1.9 Gt a−1). The rapid upglacier migration of the equilibrium line has left large areas of subsurface firn in the current ablation area and has far outpaced the ice flow response, illustrating that the ice cap is not in equilibrium and out of balance with the current climate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".