Spatio-temporal variability in elevation changes of two high-Arctic valley glaciers
Bibliographic record
Abstract
Abstract Uncertainties in estimates of glacier and ice-cap contribution to sea-level rise exist in part due to poor quantification of mass-balance errors, particularly those resulting from extrapolation of sparse measurements. Centre-line data are often assumed to be representative of the glacier as a whole, with little attention paid to extrapolation errors or their effect on mass-balance estimates. Here we present detailed digital elevation model (DEM) measurements of glacier-wide elevation changes over the last ~40 years at two glaciers on Svalbard, Norwegian Arctic. Austre Br0ggerbreen and Midtre Lovenbreen are shown to have lost 27.54 ± 0.98 and 9.65 ± 0.76 × 10 7 m 3 of ice, respectively, between 1966 and 2005, findings that we relate to trends in average summer air temperatures and winter accumulation. These volume losses correspond to geodetic balances of -0.58 ± 0.03 and -0.41 ± 0.03 mw.e. a -1 , respectively. Our analysis revealed high spatial complexity in patterns of elevation change, varying between glaciers, between measurement intervals and within and between elevation bins. Balances from extrapolated centre-line geodetic data were the same (within errors) as those from full-coverage DEM differencing in the majority of comparisons, yet significantly underestimated balance in three instances. Additionally, field mass balance from centre-line ablation stake data underestimated balances from full-coverage geodetic measurements during three of six measurement periods. These findings may support the hypothesis that field measurements underestimate Svalbard glacier mass loss, at least partly as a result of the failure of centre-line measurements to account for glacier-wide variations in ablation. Our results demonstrate the importance of deriving accurate interpolation functions and constraining extrapolation errors from sparse measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".