Glacial isostatic stress shadowing by the Antarctic ice sheet
Bibliographic record
Abstract
Numerous examples of fault slip that offset late Quaternary glacial deposits and bedrock polish support the idea that the glacial loading cycle causes earthquakes in the upper crust. A semianalytical scheme is presented for quantifying glacial and postglacial lithospheric fault reactivation using contemporary rock fracture prediction methods. It extends previous studies by considering differential Mogi‐von Mises stresses, in addition to those resulting from a Coulomb analysis. The approach utilizes gravitational viscoelastodynamic theory and explores the relationships between ice mass history and regional seismicity and faulting in a segment of East Antarctica containing the great Antarctic Plate (Balleny Island) earthquake of 25 March 1998 (Mw 8.1). Predictions of the failure stress fields within the seismogenic crust are generated for differing assumptions about background stress orientation, mantle viscosity, lithospheric thickness, and possible late Holocene deglaciation for the D91 Antarctic ice sheet history. Similar stress fracture fields are predicted by Mogi‐von Mises and Coulomb theory, thus validating previous rebound Coulomb analysis. A thick lithosphere, of the order of 150–240 km, augments stress shadowing by a late melting (middle‐late Holocene) coastal East Antarctic ice complex and could cause present‐day earthquakes many hundreds of kilometers seaward of the former Last Glacial Maximum grounding line.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".