A comparison of the last two glacial inceptions via fully coupled transient ice and climate modelling.
Bibliographic record
Abstract
Paleo records indicate significant variation in sea level and temperature proxies between different glacial cycles. What is unclear is the extent to which these differences are due to noise in the physical system versus a robust response to external forcings. When one considers what is happening with each individual ice sheet, variations between glacial cycles are largely unknown, given the few relevant records available to constrain ice sheet extent before the Eemian. To explore both the controls on past ice sheet and climate evolution and explore bounds on what the evolution might actually have looked like, we are running ensemble simulations of the last two glacial cycles with the fully coupled ice/climate model LCice. LCice is a coupled version of the Loveclim EMIC and GSM glacial systems model with hybrid shallow shelf and shallow ice flow and global visco-elastic glacio-isostatic adjustment. The current configuration includes all 4 ice sheet complexes and is subject to only orbital and greenhouse gas forcing.To answer the above questions, we present ensemble results for the last two glacial inceptions, focusing on what key ice sheet and climate characteristics are robust across the ensemble and what are not. The role of key forcings and feedbacks are also isolated through a set of sensitivity experiments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".