North American Pleistocene Glacial Erosion and Thin Pliocene Regolith Thickness Inferred from Data-Constrained Fully Coupled Ice-Climate-Sediment modelling
Bibliographic record
Abstract
Abstract. Beyond the impact of glacial isostatic adjustment, landscape evolution is typically neglected at large scale when considering the basal boundary condition for ice sheet and climate modelling over past glacial cycles. Erosion and changing sediment loads impact bed elevation, land/sea mask, and basal drag. To date, how the above affects past ice sheet evolution is unclear. Constraining the role that Pliocene regolith may have played in Pleistocene glacial cycle variability requires bounds on the amount of regolith preceding those glacial cycles. However, quantitative bounds on regolith thickness at the spatial scale of the glaciated regions of North America are currently absent in the literature. To address the above, we present an updated sediment production and transport model with dynamically calculated soft sediment mask, isostatic adjustment to dynamical sediment load and bedrock erosion, and a new subglacial hydrology model coupled to the Glacial Systems Model. The coupled model is capable of multi-million year integrations driven only by greenhouse gas concentration and insolation. The model passes a set of verification tests and conserves mass. We assess parametric sensitivity of sediment transport rates. We compare the final sediment solutions in an ensemble of whole-Pleistocene simulations for a range of initial (Pliocene) regolith thicknesses against multiple estimates for present day drift thickness distribution, Quaternary sediment volume in the Atlantic Ocean, and erosion depth estimates. Consistency of modelling and data constraints requires a Pliocene regolith thickness of less than 50 m.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".