The pre-Pleistocene North American bed from coupled ice-climate-sediment physics and its strong influence on glacial cycle evolution
Bibliographic record
Abstract
The mid-Pleistocene Transition (MPT) from 41 kyr to 100 kyr glacial cycles was one of the largest changes in the Earth system over the past 2 million years. The transition happened in the absence of a relevant change in orbital forcing. As such, it presents a challenge for the Milankovitch theory of glacial cycles. A change from a low to high friction bed under the North American Ice Complex through the removal of pre-glacial regolith has been hypothesized to play a critical role in the transition. For testing, this hypothesis requires constraint on pre-glacial regolith cover and topography as well as mechanistic constraint on whether the appropriate amount of regolith can be removed from the required regions to enable MPT occurrence at the right time. To date, however, Pleistocene regolith removal has not been simulated for a realistic, 3D North American ice sheet fully resolving relevant basal processes. A further challenge is very limited constraints on pre-glacial bed elevation and sediment thickness.Herein, we address these challenges with an appropriate computational model and ensemble-based analysis addressing parametric and initial mean sediment cover uncertainties. We use the 3D Glacial Systems Model that incorporates relevant glacial processes. Specifically, it includes: 3D thermomechanically coupled hybrid SIA/SSA ice physics, fully coupled sediment production and transport, subglacial linked-cavity and tunnel hydrology, isostatic adjustment from dynamic loading and erosion, and climate from a 2D non-linear energy balance model and glacial index. The sediment model includes quarrying and abrasion for sediment production with both englacial and subglacial transport. The coupled system is driven only by atmospheric CO2 and insolation.We show that the ice, climate, and sediment processes encapsulated in this fully coupled glacial systems model enables capture of the evolution of the Pleistocene North American glacial system. Specifically and within observational uncertainty, our model captures: the shift from 41 to 100 kyr glacial cycles, early Pleistocene extent, LGM ice volume, deglacial ice extent, and the broad present-day sediment distribution. We also find that pre-glacial sediment thickness and topography have a strong influence on the strength and duration of early Pleistocene glaciations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".