Glacial isostatic adjustment, relative sea level history and mantle viscosity: reconciling relative sea level model predictions for the U.S. East coast with geological constraints
Bibliographic record
Abstract
Models of the glacial isostatic adjustment process, which is dominated by the influence of the Late Pleistocene cycle of glaciation and deglaciation, depend on two fundamental inputs: a history of ice-sheet loading and a model of the radial variation of mantle viscosity. These models may be tested and refined by comparing their local predictions of relative sea level history to geological inferences based upon appropriate sea level indicators. The U.S. Atlantic coast is a region of particular interest in this regard, due to the fact that data from the length of this coast provides a transect of the forebulge associated with the former Laurentide ice sheet. High-quality relative sea level histories from this region are employed herein to explore the ability of current models of mantle viscosity to explain the inferred evolution of relative sea level that have accompanied forebulge collapse following deglaciation. Existing misfits are characterized, and alternatives are explored for their reconciliation. It is demonstrated that a new model of mantle viscosity, referred to herein as VM6, when coupled with the latest model of deglaciation history ICE-6G_C, is able to eliminate the majority of these misfits, while continuing to reconcile a wide range of other important geophysical observables, as well as additional relative sea level data from the North American. West coast which also record the collapse of the forebulge but which have not been employed in tuning the viscosity profile to enable ICE-6G_C (VM6) to fit the East coast data set.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".