Glaciological reconstruction of the Laurentide Ice Sheet: physical processes and modelling challenges
Bibliographic record
Abstract
Current understanding of Pleistocene ice-sheet history is based on collective inferences from three separate avenues of study: (1) the geologic and paleoceanographic records, (2) the isostatic record, and (3) the behaviour of contemporary glaciers and ice sheets. The geologic record provides good constraint on the areal extent of former ice sheets, while isostatic deflection patterns provide important information about late-glacial ice-sheet thickness. The picture emerging from geologic and isostatic deductions is suggestive of a thin and mobile Laurentide Ice Sheet relative to present-day Greenland and Antarctica. We model Laurentide Ice Sheet evolution through a glacial cycle to explore the glaciological mechanisms that are required to replicate the geologic and isostatic evidence. A number of glaciological processes important to the ice-sheet evolution are not fully understood, including marine-based ice dynamics, iceberg calving, rheologic properties of ice, and basal flow dynamics. We present a spectrum of glacial cycle simulations with different treatments of poorly constrained physical processes. We conclude that glaciological model reconstructions can only be reconciled with the late-glacial geologic record of a thin, low-sloping Laurentide Ice Sheet by invoking (1) extremely deformable ice, (2) widespread basal flow, or (3) paleoclimate-ice-sheet fluctuations which give last glacial maximum ice sheets that are far from equilibrium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".