EastGRIP ice core reveals the exceptional evolution of crystallographic preferred orientation throughout the Northeast Greenland Ice Stream
Bibliographic record
Abstract
Abstract. A better understanding of glacial ice flow and how it is influenced by internal deformation is required to improve the projections of future sea-level rise in a warming climate. Especially large ice streams, the main contributors to solid ice discharge to the ocean, still require more observational data to be represented sufficiently in numerical ice-sheet models. The East Greenland Ice-core Project (EastGRIP) successfully drilled the first continuous deep ice core through an active ice stream, the Northeast Greenland Ice Stream (NEGIS), focusing on investigating the dynamical processes that lead to its exceptionally high velocity. Here, we show Crystallographic Preferred Orientations (CPO) data in 5–15 m depth resolution throughout 2663 m, down to bedrock, to determine the deformation regimes in this ice stream setting complemented by grain-size and borehole temperature profiles for context. A broad single-maximum CPO pattern is present in the upper 200 m caused by overlying snow and ice layers. Below, a crossed girdle CPO is observed for the first time in a deep ice core and we discuss possible formation mechanisms. Between 500 and 1230 m of depth, we observe a vertical girdle CPO indicative of along-flow extensional deformation. A complementary simple-shear component and polygonization explain the CPO between 1230 and 2500 m, a vertical girdle with horizontal maxima of varying strength. Close to bedrock, a multi-maxima CPO originates from migration recrystallisation due to high temperatures close to the pressure melting point. Ice at this depth is characterised by centimetre-large, amoeboid-shaped grains, which, together with the conductivity data from the deepest 260 m, indicates that the core contains ice from the last Eemian. A comparison with other deep ice cores from Greenland and Antarctica shows the uniquely fast development of CPO at shallow depths in the EastGRIP ice core due to its location in an area of high strain rates while the grain-size evolution with depth remains similar to less dynamic sites confirming that it is mainly governed by the varying purity of ice deposited during varying climatic conditions. We further show that the overall plug flow of NEGIS is characterised by many small-scale variations, which remain to be considered in ice-flow models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".