Editorial: Observational Assessments of Glacier Mass Changes at Regional and Global Level
Bibliographic record
Abstract
Observational Assessments of Glacier Mass Changes at Regional and Global Level.Glaciers represent a measurable indicator of the spatial and temporal patterns of global climate variability.Those distinct from the Greenland and Antarctic Ice Sheets cover an area of approximately 706,000 km 2 globally (RGI Consortium, 2017), with an estimated total volume of 170 ± 21 × 10 3 km 3 , or 0.43 ± 0.06 m of potential sea-level rise equivalent (Huss and Farinotti, 2012).Retreating and thinning glaciers are icons of climate change and affect the local hazard scenario, regional water resources and glacier runoff as well as changes in global sea level.Techniques for measuring and monitoring changes to glaciers over the last century have expanded from in situ point measurements of snow accumulation and ice ablation to large regional-and globalscale surveys employing remote sensing and modeling approaches, which have shaped our understanding of world-wide glacier changes.Today, the Gravity Recovery and Climate Experiment (GRACE) mission can be used to derive monthly regional mass changes (Wouters et al.) and has proven to be particularly effective in detecting glacier mass changes over regions with extensive ice cover (Alaska, Canadian Arctic, Russian Arctic, Svalbard, Iceland, the Southern Andes, and High Mountain Asia).However, Wouters et al. note that GRACE cannot resolve the signal from peripheral glaciers of the Greenland and Antarctic ice sheets and struggles to detect statistically significant signals in mountain ranges with smaller glacier covers due to weaker signals and relatively greater background noise and uncertainty.Increasingly, geodetic mass-balance records are filling the spatial-scale gap between coarseresolution gravimetry and point-based glaciological mass-balance records.Advances in digital elevation model creation, automation, and analysis from historic and contemporary sources are driving a notable increase in the availability of geodetic mass-balance records from around the world.Indeed, original works within this Research Topic alone represent 9,908 new geodetic mass balance contributions to the World Glacier Monitoring Service (WGMS) and the IPCC AR6, notably from regions of Greenland (Huber et al.), the European Alps (Davaze et al.), Iceland (Belart et al.), Northern Tien Shan (Kapitsa et al.) and the Patagonian Andes (Falashi et al.), as well as ∼8,000 updated geodetic records from the Central Andes (Ferri et al.). Advances in digital photogrammetry and improved accuracies in the 3D alignment of elevation models have rekindled the scientific value inherent to historical maps, aerial photography and declassified spy satellite imagery as exemplified by Falashi et al., Huber et al., Belart et al., and Kapitsa et al.These advances extend the temporal reach
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.031 | 0.027 |
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".