Validation of high-resolution GRACE mascon estimates of glacier mass changes in the St Elias Mountains, Alaska, USA, using aircraft laser altimetry
Bibliographic record
Abstract
Abstract We acquired center-line surface elevations from glaciers in the St Elias Mountains of Alaska/northwestern Canada using aircraft laser altimetry during 2000–05, and compared these with repeat measurements acquired in 2007. The resulting elevation changes were used to estimate the mass balance of 32 900 km 2 of glaciers in the St Elias Mountains during September 2003 to August 2007, yielding a value of −21.2 ± 3.8 Gt a −1 , equivalent to an area-averaged mass balance of −0.64 ± 0.12 m a −1 water equivalent (w.e.). High-resolution (2 arc-degrees spatial and 10 day temporal) Gravity Recovery and Climate Experiment (GRACE) mass-balance estimates during this time period were scaled to glaciers of the St Elias Mountains, yielding a value of −20.6 ± 3.0 Gt a −1 , or an area-averaged mass balance of −0.63 ± 0.09 m a −1 w.e. The difference in balance estimates (altimetry minus GRACE) was −0.6 ± 4.8 Gt a −1 , well within the estimated errors. Differences likely resulted from uncertainties in subgrid sampling of the GRACE mass concentration (mascon) solutions, and from errors in assigning an appropriate near-surface density in the altimetry estimates. The good correspondence between GRACE and aircraft altimetry data suggests that high-resolution GRACE mascon solutions can be used to accurately assess mass-balance trends of mountain glacier regions that are undergoing large changes.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".