Alaskan Glacier Depths from a Decade of Airborne Radar Sounding
Bibliographic record
Abstract
NASA’s Operation IceBridge employed airborne radar sounders in Alaska and adjacent northwestern Canada between 2012-2021 to measure the thickness of the region’s glaciers. Here we present the first comprehensive analysis of these data, providing over 5,500 linear-km of ice thickness and bed elevation measurements – constituting the greatest ice thickness inventory for this region to date. Aside from glaciers of the Saint Elias Mountains, radar bed returns are limited to expansive accumulation areas and glacier termini, distant from sources of off-nadir surface topography. Gridded measurements across Bering Glacier reveal a subglacial trough extending over 50 km from the glacier's terminus up to the Bagley Ice Valley, likely a subglacial expression of the Bering Fault. We find that many of the glacier termini successfully sounded by Operation IceBridge have overdeepened beds, which may offer insight into the potential extent of proglacial lakes and associated natural hazards given continued thinning and retreat. While the long-wavelength sounders employed by Operation IceBridge have proven capable of sounding through nearly 1500 m of temperate ice, radar surface returns from the flanks of the region’s mountain glaciers remain the greatest challenge to identifying glacier bed returns and retrieving ice thickness measurements. Simulating these returns in the survey planning may significantly improve the mapping success of future airborne radar campaigns.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".