GLAC3: Joint glaciological model and visco-elastic earth model history matching of the last glacial cycle: Greenland and Antarctica components
Bibliographic record
Abstract
We present the Antarctic and Greenland components of an extensivehistory matching for last glacial cycle evolution and regional earthrheology from glaciological modelling with fully coupled regionalvisco-elastic glacio-isostatic adjustment. Of further distinction isthe accounting for model structural uncertainty. The product is a highvariance set of joint chronologies and earth model parameter vectorsthat are not inconsistent with available constraints givenobservational and model uncertainties.Ensemble parameters are from Markov Chain Monte Carlo sampling withBayesian artificial neural network emulators. The glaciological modelis the Glacial Systems Model with hybrid shallow shelf and shallow icephysics and a coupled energy balance climate model. It includes a muchlarger set of ensemble parameters (34 and 38 respectively forGreenland and Antarctica) than other paleo ice sheet models tofacilitate more complete assessment of past ice sheet evolutionuncertainty. The history matching is against a large curated set ofrelative sealevel, vertical velocity, cosmogenic age, and marineconstraints as well as the present-day physical and thermalconfiguration of the ice sheet.The careful assessment of uncertainties, breadth of modelledprocesses, and sampling approach has resulted in NROY (not ruled outyet) chronologies and rheological inferences that contradict previousmore limited model-based reconstructions. For instance, in contrastto most previous inferences for the Antarctic contribution to the lastglacial maximum (LGM) low-stand (with inferred values of about 10 m iceequivalent sea-level (mESL), our NROY set includes chronologies withLGM contributions of up to 23 mESL. This result represents apotentially significant contribution towards addressing the challengeof LGM missing ice.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".