Resolving discrepancies between field and modelled relative sea‐level data: lessons from western Ireland
Bibliographic record
Abstract
ABSTRACT Accurate reconstruction of Lateglacial and Holocene relative sea‐level (RSL) histories is complicated where mismatches exist between geological data and RSL curves generated by models of glacio‐isostatic adjustment (GIA). In Ireland, such discrepancies have profound implications for interpreting the glacial history of the British Isles and for the use of glacial rebound models to predict future sea‐level changes. To address this issue we present new RSL data from four sites along the western coast of Ireland, including 17 data points from the critical period before 5000 14C a BP for which very few data are available. We generate new RSL simulations from an existing GIA model, incorporating a thickened Irish Ice sheet component. Simulated curves from Co. Mayo and Co. Donegal accommodate the higher than present Lateglacial RSL inferred from glaciomarine muds while still meeting the requirement for below present RSL indicated by the new terrestrial limiting data points. Relaxation of trimline constraints on maximum ice sheet thickness provides considerable scope for improved GIA performance. These results demonstrate inferences about RSL drawn from GIA modelling and glacio‐sedimentary data are not mutually exclusive, and represent a significant step towards resolving a long‐standing debate between the field‐based and modelling communities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".