Bibliographic record
Abstract
The Northeast Greenland Ice Stream (NEGIS) is the largest active ice stream on the Greenland Ice Sheet (GrIS) and 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 can introduce climatic bias into ice cores through the advection of ice deposited under different conditions further upstream. This is particularly true for EastGRIP due to its location inside an ice stream on the eastern flank of the GrIS. Understanding and ultimately correcting for such effects requires information on the atmospheric conditions at the time and location of snow 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 (GRIP) and EastGRIP. Isochrones are traced in radio-echo-sounding images along these flow lines and dated with the GRIP and EastGRIP ice-core chronologies. The observed depth–age relationship constrains the Monte Carlo method which is used to determine unknown model parameters. We calculate backward-in-time particle trajectories to determine the source location of ice found in the EastGRIP ice core and present estimates of surface elevation and past accumulation rates at the deposition site. Our results indicate that increased snow accumulation with increasing upstream distance is predominantly responsible for the constant annual layer thicknesses observed in the upper part of the ice column at EastGRIP, and the inverted model parameters suggest that basal melting and sliding are important factors determining ice flow in the NEGIS. The results of this study form a basis for applying upstream corrections to a variety of ice-core measurements, and the inverted model parameters are useful constraints for more sophisticated modelling 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.031 | 0.013 |
| Insufficient payload (model declined to judge) | 0.135 | 0.116 |
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".