Upstream flow effects revealed in the EastGRIP ice-core using a Monte Carlo inversion of a 2D ice-flow model
Bibliographic record
Abstract
The North East Greenland ice-stream (NEGIS) is the largest active ice-stream on the Greenland ice-sheet and is a crucial contributor to the ice-sheet mass balance. To investigate the ice-stream dynamics and to gain information about the past climate, a deep ice-core is drilled in the upstream part of the NEGIS, termed the East Greenland ice-core project (EastGRIP). Upstream flow effects introduce non-climatic bias in ice-cores and are particularly strong at EastGRIP due to high ice-flow velocities and the location in an ice-stream on the eastern flank of the Greenland ice-sheet. Understanding and ultimately correcting for such effects requires information on the source area and the local atmospheric conditions at the time of ice deposition. We use a two-dimensional Dansgaard-Johnsen model to simulate ice-flow along three approximated flow-lines between the summit of the ice-sheet and EastGRIP. Model parameters are determined using a Monte Carlo inversion by minimizing the misfit between modeled isochrones and isochrones observed in radio-echo-sounding images. We calculate backward-in-time particle trajectories to determine the source area of ice found in the EastGRIP core today and present estimates of surface elevation and past accumulation-rates at the deposition site. The thinning function and accumulated strain obtained from the modeled velocity field provide useful information on the deformation history in the EastGRIP ice. Our results indicate that increased accumulation in the upstream area is predominantly responsible for the constant annual layer thickness observed in the upper part of the ice column at EastGRIP. Inverted model parameters suggest that the imprint of basal melting and sliding is present in large parts along the flow profiles and that most internal ice deformation happens close to the bedrock. The results of this study can act as a basis for applying upstream corrections to a variety of ice-core measurements, and the model parameters can be useful constraints for more sophisticated modeling approaches in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".