Evolution of a coupled marine ice sheet–sea level model
Bibliographic record
Abstract
We investigate the stability of marine ice sheets by coupling a gravitationally self‐consistent sea level model valid for a self‐gravitating, viscoelastically deforming Earth to a 1‐D marine ice sheet‐shelf model. The evolution of the coupled model is explored for a suite of simulations in which we vary the bed slope and the forcing that initiates retreat. We find that the sea level fall at the grounding line associated with a retreating ice sheet acts to slow the retreat; in simulations with shallow reversed bed slopes and/or small external forcing, the drop in sea level can be sufficient to halt the retreat. The rate of sea level change at the grounding line has an elastic component due to ongoing changes in ice sheet geometry, and a viscous component due to past ice and ocean load changes. When the ice sheet model is forced from steady state, on short timescales (<∼500 years), viscous effects may be ignored and grounding‐line migration at a given time will depend on the local bedrock topography and on contemporaneous sea level changes driven by ongoing ice sheet mass flux. On longer timescales, an accurate assessment of the present stability of a marine ice sheet requires knowledge of its past evolution.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".