History Matching of the Last Glacial Cycle Model for the Icelandic and Patagonian Ice Sheets
Bibliographic record
Abstract
To date, the Icelandic Ice Sheet (IIS) and Patagonian Ice Sheet (PIS) have been poorly understood with regard to their configuration, dynamics, and evolution during the last glacial cycle. The few glaciological modelling studies of the IIS and PIS to date have placed minimal attention on addressing model uncertainties. As such, their inferential value is poorly interpretable. To address this, we present the results of history matchings of the 3D Glacial Systems Model (GSM) against curated sets of paleo constraints for the last glacial cycle IIS and PIS. History matching identifies a set of model simulations that are not ruled out given available data constraints and robust uncertainty analysis (including both model and data uncertainties). As such, it aims to “bracket reality” as opposed to the much more difficult task of determining a meaningful most likely chronology.The GSM is a thermo-mechanically coupled glaciological model with hybrid shallow ice and shallow shelf/stream physics. The climate forcing consists of a fully coupled energy balance climate model and glacial indexed climate forcing using the results of PMIP3 (Paleo Model Intercomparison Project). Approximate 30 GSM ensemble parameters partially account for uncertainties in climate, basal drag, and marine ice processes. The GSM configuration includes fully coupled visco-elastic glacio-isostatic adjustment enabling physically self-consistent relative sealevel predictions. Our presentation focuses on bracketing chronologies for the last glacial cycle IIS and PIS as well as disentangling the relative contribution of atmospheric and marine forcings on mass loss during the deglaciation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".