Glacier changes in Alaska: can mass-balance models explain GRACE mascon trends?
Bibliographic record
Abstract
Abstract Temperature and precipitation data from three weather stations in the St Elias Mountains of Alaska and northwestern Canada were used to drive one-dimensional (1-D) (elevation-dependent) and 0-D degree-day mass-balance models. Model outputs were optimized against a 10 day resolution time series of mass variability during 2003–07 obtained from Gravity Recovery and Climate Experiment (GRACE) mass concentration (mascon) solutions. The models explained 52–60% of the variance in the GRACE time series. Modelled mass variations matched the phase of the GRACE observations, and all optimized model parameters were within the range of values determined from conventional mass-balance and meteorological observations. We describe a framework for selecting appropriate weather stations and mass-balance models to represent glacier variations of large regions. There is potential for extending these calibrated mass-balance models forwards or backwards in time to construct mass-balance time series outside of the GRACE measurement window.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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 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".