Reconstruction of the palaeo‐sea level of Britain and Ireland arising from empirical constraints of ice extent: implications for regional sea level forecasts and North American ice sheet volume
Bibliographic record
Abstract
ABSTRACT Reconstructions of palaeo‐sea level are vital for predicting future sea level change and constraining palaeo‐ice sheet reconstructions, as well as being useful for a wide array of applications across Quaternary Science. Previous reconstructions of the palaeo‐sea level of Britain and Ireland relied on a circular tuning of glacio‐isostatic models: input ice sheet thicknesses and extents were iteratively altered to fit relative sea level data. Here we break that circularity by utilizing new data from the BRITICE‐CHRONO project, which constrains the position of the British–Irish ice sheet margin through time, and we compare derived glacio‐isostatic modelling to the rich relative sea level record. We test a combination of plausible ice thickness scenarios which account for the uncertainty of ice margin position over the North Sea, demonstrating the region where regional sea level data could distinguish between different glaciation scenarios. Our optimal reconstruction is then combined with several global‐scale reconstructions. As the signal of the British–Irish Ice Sheet is constrained, we demonstrate how the relative sea level record of Britain and Ireland can be used to test reconstructions of far‐field ice sheets (e.g. Antarctica, Eurasia and the Laurentide). The derived palaeo‐topography data are likely to be useful for multiple disciplines. Finally, our improved method of sea level reconstruction impacts predictions of contemporary vertical land motion.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".