Mass budget of the glaciers and ice caps of the Queen Elizabeth Islands, Canada, from 1991 to 2015
Bibliographic record
Abstract
Recent studies indicate that the glaciers and ice caps in Queen Elizabeth Islands (QEI), Canada have experienced an increase in ice mass loss during the last two decades, but the contribution of ice dynamics to this loss is not well known. We present a comprehensive mapping of ice velocity using a suite of satellite data from year 1991 to 2015, combined with ice thickness data from NASA Operation IceBridge, to calculate ice discharge. We find that ice discharge increased significantly after 2011 in Prince of Wales Icefield, maintained or decreased in other sectors, whereas glacier surges have little impact on long-term trends in ice discharge. During 1991–2005, the QEI mass loss averaged 6.3 ± 1.1 Gt yr −1 , 52% from ice discharge and the rest from surface mass balance (SMB). During 2005–2014, the mass loss from ice discharge averaged 3.5 ± 0.2 Gt yr −1 (10%) versus 29.6 ± 3.0 Gt yr −1 (90%) from SMB. SMB processes therefore dominate the QEI mass balance, with ice dynamics playing a significant role only in a few basins.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".