Laurentide Ice Saddle Mergers Drive Rapid Sea Level Drops During Glaciations
Bibliographic record
Abstract
Abstract Recent glacial periods have included several periods of rapid sea level drop that are still not well understood. Here we show that rapid sea level drops can occur due to merger of two separate ice sheets without correspondingly rapid climate forcing. Using the Parallel Ice Sheet Model (PISM), we simulate glaciation of the Laurentide Ice Sheet (LIS) by gradually decreasing equilibrium‐line altitude (ELA). Merger of the extended Keewatin sector of the Laurentide Ice Sheet with Labrador sector of the Laurentide Ice Sheet south of Hudson Bay causes a positive feedback between increasing surface elevation and increasing surface mass balance in the merger region, leading to fast ice sheet growth. The simulated saddle merger of LIS lowers sea level by 20 m in less than 20 kyr with periods of sea level fall exceeding 2 m/kyr, similar to those observed in paleo‐sea level records.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".