Improving estimation of glacier volume change: a GLIMS case study of Bering Glacier System, Alaska
Bibliographic record
Abstract
Abstract. The Global Land Ice Measurements from Space (GLIMS) project has developed tools and methods that can be employed by analysts to create accurate glacier outlines. To illustrate the importance of accurate glacier outlines and the effectiveness of GLIMS standards we conducted a case study on Bering Glacier System (BGS), Alaska. BGS is a complex glacier system aggregated from multiple drainage basins, numerous tributaries, and many accumulation areas. Published measurements of BGS surface area vary from 1740 to 6200 km2, depending on how the boundaries of this system have been defined. Utilizing GLIMS tools and standards we have completed a new outline (3630 km2) and analysis of the area-altitude distribution (hypsometry) of BGS using Landsat images from 2000 and 2001 and a US Geological Survey 15-min digital elevation model. We compared this new hypsometry with three different hypsometries to illustrate the errors that result from the widely varying estimates of BGS extent. The use of different BGS hypsometries results in highly variable measures of volume change and net balance (bn). Applying a simple hypsometry-dependent mass-balance model to different hypsometries results in a bn rate range of −1.0 to −3.1 m a−1 water equivalent (W.E.), a volume change range of −3.8 to −6.7 km3 a−1 W.E., and a near doubling in contributions to sea level equivalent, 0.011 mm a−1 to 0.019 mm a−1. Current inaccuracies in glacier outlines hinder our ability to correctly quantify glacier change. Understanding of glacier extents can become comprehensive and accurate. Such accuracy is possible with the increasing volume of satellite imagery of glacierized regions, recent advances in tools and standards, and dedication to this important task.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".