Relative sea-level sensitivity in the Eurasian region to Earth and ice-sheet model uncertainty during the Last Interglacial
Bibliographic record
Abstract
Fingerprinting the source and rate of the melt of polar ice sheets during the Last Interglacial is a key research challenge. This is reliant on high-quality relative sea-level constraints, and the correction of this data for the effects of glacial isostatic adjustment driven by ice sheet cover changes prior to the interglacial. However, both the spatial and temporal evolution of past ice sheets and the Earth’s rheological structure that serve as inputs to glacial isostatic adjustment predictions are significantly uncertain. This study sets out to determine the relative influence of each of these inputs on modelled values of Last Interglacial relative sea levels and how this influence varies spatially. To answer this question, we use a palaeo ice-sheet model and a gravitationally consistent glacial isostatic adjustment model. We develop new numerical tools to generate plausible ice-sheet extent and histories, quantify relative sea-level uncertainty, and perform a Sobol sensitivity analysis facilitated by the use of Gaussian process emulation. We find that Earth model parameters are the dominant contributors to relative sea-level uncertainty in most Eurasian regions, but that relative sea-level values in the Barents-Kara Sea are most influenced by ice-sheet loading, while the timing of the deglaciation has the greatest impact in the Baltic Sea. Our results show that the magnitude and rate of relative sea-level change is relatively insensitive to the specific timing of ice-sheet retreat, as well as the configuration of the far-field North American ice sheet. Overall, our work suggests that the coastlines of the southern North Sea and the English Channel are least influenced by relative sea-level uncertainty and are the most suitable for future data collection studies aiming to limit the influence of glacial isostatic adjustment.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".