Parameters controlling mid-Holocene highstand in Glacial Isostatic Adjustment modelling
Bibliographic record
Abstract
Holocene relative sea-level (RSL) records from regions distal from ice sheets (far-field) are commonly characterized by a mid-Holocene highstand, when RSL reached higher than present levels. The magnitude and timing of the mid-Holocene highstand varies spatially due to hydro-isostatic processes including ocean syphoning and continental levering. While there are open questions regarding the timing, magnitude and source of ice-equivalent sea level in the middle to late Holocene. Here, we compare Glacial Isostatic Adjustment (GIA) model predictions to a standardized database of sea-level index points (SLIPs) from Southeast Asia where we have near-complete Holocene records. The database has more than 130 SLIPs that span the time period from ~9.5 ka BP to present. We investigate the sensitivity of mid-Holocene RSL predictions to GIA parameters, including the lateral lithospheric thickness variation, mantle viscosity (both 1D and 3D), and deglaciation history from different ice sheets (e.g., Laurentide, Fennoscandia, Antarctica). We compute gravitationally self-consistent RSL histories for the GIA model with time dependent coastlines and rotational feedback using the Coupled Laplace-Finite Element Method. The preliminary results show that the timing of the highstand is mainly controlled by the deglaciation history (ice-equivalent sea level), while the magnitude is dominated by Earth parameters (e.g., lithospheric thickness, mantle viscosity). We further investigate whether there is meltwater input during middle to late Holocene and whether the RSL records from Southeast Asia can reveal the meltwater source, like Antarctica.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".