Bibliographic record
Abstract
Abstract. In this study we present the evolution of the Antarctic Ice Sheet (AIS) since the last interglacial. This is achieved by means of a history-matching analysis where a newly updated observational database (AntICE2, Lecavalier et al., 2023) was used to constrain a large ensemble of 9,293 model simulations. The Glacial Systems Model (GSM) configured with 38 ensemble parameters was history matched against observations of past ice extent, past ice thickness, past sea level, ice core borehole temperature profiles, present-day uplift rates, and present-day ice sheet geometry and surface velocity. Successive ensembles were used to train Bayesian Artificial Neural Network emulators. The parameter space was efficiently explored to identify the most relevant portions of the parameter space through Markov Chain Monte Carlo sampling with the emulators. The history matching ruled out model simulations which were inconsistent with the observational constraint database. During the Last Interglaciation (LIG), the AIS yielded several meters equivalent sea-level (mESL) grounded ice volume deficit relative to present with subsurface ocean warming during this period being the key uncertainty. At the global Last Glacial Maximum (LGM), the best-fitting sub-ensemble of AIS simulations reached an excess grounded ice volume relative to present of 9.2 to 26.5 mESL. Considering the data does not rule out simulations with an LGM grounded ice volume > 20 mESL with respect to present, the AIS volume at the LGM can partly explain the missing ice problem and help close the LGM sea-level budget. Moreover, during the deglaciation, the state space estimation of the AIS based on the GSM and near-field observational constraints allow only a negligible Antarctic Melt Water Pulse 1a contribution (-0.2 to 0.3 mESL).
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.216 | 0.160 |
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".