Seismic evidence for complex sedimentary control of Greenland Ice Sheet flow
Bibliographic record
Abstract
The land-terminating margin of the Greenland Ice Sheet has slowed down in recent decades, although the causes and implications for future ice flow are unclear. Explained originally by a self-regulating mechanism where basal slip reduces as drainage evolves from low to high efficiency, recent numerical modeling invokes a sedimentary control of ice sheet flow as an alternative hypothesis. Although both hypotheses can explain the recent slowdown, their respective forecasts of a long-term deceleration versus an acceleration of ice flow are contradictory. We present amplitude-versus-angle seismic data as the first observational test of the alternative hypothesis. We document transient modifications of basal sediment strengths by rapid subglacial drainages of supraglacial lakes, the primary current control on summer ice sheet flow according to our numerical model. Our observations agree with simulations of initial postdrainage sediment weakening and ice flow accelerations, and subsequent sediment restrengthening and ice flow decelerations, and thus confirm the alternative hypothesis. Although simulated melt season acceleration of ice flow due to weakening of subglacial sediments does not currently outweigh winter slowdown forced by self-regulation, they could dominate over the longer term. Subglacial sediments beneath the Greenland Ice Sheet must therefore be mapped and characterized, and a sedimentary control of ice flow must be evaluated against competing self-regulation mechanisms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".