High Spatial Resolution Mapping of Glaciers Mass Variations over the Gulf of Alaska Using Spatial Concentration Functions and Monthly GRACE and GRACE-FO Data 
Bibliographic record
Abstract
The spatial resolutions of GRACE solutions (level-2 dampened by truncation and/or filtering and level-3 called MASCON) are generally too coarse (~300km) to estimate regional changes in the terrestrial water storage (TWS) components. Focusing methods such as constrained forward approach and spatial concentration functions could improve the spatial distribution estimates of concentrated masses (e.g. glaciers, lakes). In this study, we apply spatial concentration functions to create high resolution monthly time-series of glaciers mass changes over the Gulf Of Alaska (GOA). Spatial weighting functions are based on heterogeneous glaciers mass distribution maps called a priori. First, we use three a priori of different spatial resolutions and sources to create different spatial functions. Second, we compare the amplitude of glaciers mass variations with others GRACE TWS components over the GOA, using the same spatial functions, to improve the discrimination of glaciers mass distribution. Third, we use a variety of GRACE solutions with different processing assumptions given identical resolution characteristics to estimate the uncertainties associated with our methodological framework. To analyze the accuracy of our assessments, we also compare trends resulting from the spatial concentration functions and the constrained forward approach. Then, we compare our estimates with three released MASCON solutions and published results over: (i) the GOA, (ii) the Saint-Elias Mountains and (iii) the Upper Yukon watershed. The results indicate that the spatial functions are sensitive to glaciers mass distribution. The signal from the glaciers dominate the GRACE TWS over the GOA. All solutions used, provide comparable glaciers mass variations. The two focusing methods give similar trends, but the constrained forward approach is time-consuming. The results obtained here could provide to be useful to further our understanding of the contribution of the GOA’s glaciers to sea-level rise and to river flows at the regional scale.
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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 | 0.000 | 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".