High-resolution ice thickness and bed topography of a land-terminating section of the Greenland Ice Sheet
Bibliographic record
Abstract
Abstract. We present ice thickness and bed topography maps with a high spatial resolution (250–500 m) of a land-terminating section of the Greenland Ice Sheet derived from ground-based and airborne radar surveys. The data have a total area of ~12 000 km2 and cover the whole ablation area of the outlet glaciers of Isunnguata Sermia, Russell, Leverett, Ørkendalen and Isorlersuup up to the long-term mass balance equilibrium line altitude at ~1600 m above sea level. The bed topography shows highly variable subglacial trough systems, and the trough of Isunnguata Sermia Glacier is overdeepened and reaches an elevation of ~500 m below sea level. The ice surface is smooth and only reflects the bedrock topography in a subtle way, resulting in a highly variable ice thickness. The southern part of our study area consists of higher bed elevations compared to the northern part. The compiled data sets of ground-based and airborne radar surveys cover one of the most studied regions of the Greenland Ice Sheet and can be valuable for detailed studies of ice sheet dynamics and hydrology. The combined data set is freely available at doi:10.1594/pangaea.830314.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".