Regional and global volumes of glaciers derived from statistical upscaling of glacier inventory data
Bibliographic record
Abstract
Very few global‐scale ice volume estimates are available for mountain glaciers and ice caps, although such estimates are crucial for any attempts to project their contribution to sea level rise in the future. We present a statistical method for deriving regional and global ice volumes from regional glacier area distributions and volume area scaling using glacier area data from ∼123,000 glaciers from a recently extended World Glacier Inventory. We compute glacier volumes and their sea level equivalent (SLE) for 19 glacierized regions containing all mountain glaciers and ice caps on Earth. On the basis of total glacierized area of 741 × 103 ± 68 × 103 km2, we estimate a total ice volume of 241 × 103 ± 29 × 103 km3, corresponding to 0.60 ± 0.07 m SLE, of which 32% is due to glaciers in Greenland and Antarctica apart from the ice sheets. However, our estimate is sensitive to assumptions on volume area scaling coefficients and glacier area distributions in the regions that are poorly inventoried, i.e., Antarctica, North America, Greenland, and Patagonia. This emphasizes the need for more volume observations, especially of large glaciers and a more complete World Glacier Inventory in order to reduce uncertainties and to arrive at firmer volume estimates for all mountain glaciers and ice caps.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".