Pre-LGM Northern Hemisphere ice sheet topography
Bibliographic record
Abstract
Abstract. We here reconstruct the paleotopography of Northern Hemisphere ice sheets during the glacial maxima of marine isotope stages (MIS) 5b and 4.We employ a combined approach, blending geologically based reconstruction and numerical modeling, to arrive at probable ice sheet extents and topographies for each of these two time slices. For a physically based 3-D calculation based on geologically derived 2-D constraints, we use the University of Maine Ice Sheet Model (UMISM) to calculate ice sheet thickness and topography. The approach and ice sheet modeling strategy is designed to provide robust data sets of sufficient resolution for atmospheric circulation experiments for these previously elusive time periods. Two tunable parameters, a temperature scaling function applied to a spliced Vostok–GRIP record, and spatial adjustment of the climatic pole position, were employed iteratively to achieve a good fit to geological constraints where such were available. The model credibly reproduces the first-order pattern of size and location of geologically indicated ice sheets during marine isotope stages (MIS) 5b (86.2 kyr model age) and 4 (64 kyr model age). From the interglacial state of two north–south obstacles to atmospheric circulation (Rocky Mountains and Greenland), by MIS 5b the emergence of combined Quebec–central Arctic and Scandinavian–Barents-Kara ice sheets had increased the number of such highland obstacles to four. The number of major ice sheets remained constant through MIS 4, but the merging of the Cordilleran and the proto-Laurentide Ice Sheet produced a single continent-wide North American ice sheet at the LGM.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".