Spread of Svalbard Glacier Mass Loss to Barents Sea Margins Revealed by CryoSat‐2
Bibliographic record
Abstract
The Norwegian Arctic archipelago of Svalbard is located in the most rapidly warming area of the Arctic, at the interface of Arctic and Atlantic air and ocean masses. The presence of a large number of surge‐type glaciers and the potential for rapid changes in surface mass balance and ice dynamics necessitates regularly updated mass balance assessment. This study uses swath processing of CryoSat‐2 SARIn mode data to obtain glacier elevations for 2011–2017. Individual elevation estimates are collected into 1‐km2 grid cells, and a least squares plane‐fitting technique is used to calculate rates of elevation change, with residuals being used to reveal the temporal pattern. A 7‐year rate of mass change of −16.0 ± 3.0 Gt a−1 is estimated (equivalent to 0.044 mm a−1 of global sea level rise), of which −11.0 Gt a−1 results from the melt and dynamic thinning of nonsurging ice and −5.0 Gt a−1 results from surges. This compares to ‐3.4 Gt a−1 previously estimated using ice, cloud, and land elevation satellite (ICESat) (2003–2008). The west coast remains a major contributor to mass loss from nonsurging ice in the archipelago, with mass loss increasing from areas bordering the Barents Sea. Sea ice concentration and climate reanalysis data sets show ocean and lower atmospheric warming and sea ice decline in this region, likely contributing to enhanced glacier melt and discharge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".