Mass balance of the Prince of Wales Icefield, Ellesmere Island, Nunavut, Canada
Bibliographic record
Abstract
This paper estimates the mass balance of the Prince of Wales Icefield, Ellesmere Island, Canada, averaged over four decades, from measurements of surface mass balance (SMB) and iceberg calving. Shallow ice core net accumulation measurements and annual mass balance stake measurements are used in conjunction with a digital elevation model and knowledge of the location of the dominant moisture source for precipitation over the ice cap to interpolate and extrapolate spatial patterns of SMB across the Prince of Wales Icefield. The contribution of iceberg calving to the mass balance is calculated from estimates of (1) the annual volume of ice discharged at the major tidewater glacier termini and (2) the annual volume loss or gain due to terminus fluctuations. Two different approaches to determining the SMB conclude that the SMB of the ice field is approximately in balance (average equals −0.1 ± 0.4 km3 w.e. a−1, where w.e. means water equivalent) largely because of its proximity to the main year‐round moisture source that is the Smith Sound portion of the North Open Water polynya. Iceberg calving is a highly significant component of mass loss (−1.9 ± 0.2 km3 w.e. a−1) and is sufficient to make the overall mass balance of the ice field averaged over the period 1963–2003 clearly negative (−2 ± 0.45 km3 w.e. a−1, equivalent to a mean‐specific mass balance across the ice field of −0.1 m w.e. a−1). The Prince of Wales Icefield contributes ∼0.005 mm a−1 to global eustatic sea level rise.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".