Revised Estimates of Recent Mass Loss Rates for Penny Ice Cap, Baffin Island, Based on Elevation Changes Modified for Firn Densification
Bibliographic record
Abstract
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. We use the ATM L1B elevation dataset from which we extract the elevation every 10 m along a line of best fit for the 2005, 2013 and 2014 data sets. The changes in elevation (dh/dt) between 2005-2013 and 2013-2014 are calculated, then extrapolated to the entire ice cap using least-squares linear regression of dh/dt against the altimetry elevation. Dual-frequency GPS measurements and temporal changes in ice core density profiles are used to calculate firn densification and ice dynamics to isolate the component of elevation change due to surface mass balance. A Trimble R7 dGPS receiver is used with a minimum 20 minute occupation time per stake (accuracy: ± 0.09 m horizontally and ± 0.10 m vertically). We use data from ice or firn cores collected in 1995, 2010 and 2013 near the summit of the ice cap. The densification rate is calculated from the change in thickness of near-surface firn layers down to a depth equivalent to 5 m w.e. Envisat satellite imagery and ground-penetrating radar data are used to delineate the areas impacted by firn densification. These data are compared to annual in situ mass balance data collected between 2006-2014 at stakes along three survey lines during spring (~April), totalling 140 measurements at elevations ranging from 71-1822 m a.s.l.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".