Structural Evolution During Cyclic Glacier Surges: 2. Numerical Modeling
Bibliographic record
Abstract
The mesoscale structures of a glacier express the history of flow, temperature, and stress. Thus, in principle, numerical ice dynamics models have sufficient physics to examine the formation and transport of these structures. In this study we use a vertically integrated thermomechanical ice dynamics model to simulate the temporally evolving patterns of surficial moraine, stratification, foliation, and folding of glacier ice, and the density and orientation of traces of former crevasses. The modeled glaciers are simplified versions of Trapridge Glacier in northwest Canada that allow diagnostic modeling of influences on glacier structure and help to clarify the physics and numerics. In the model, surges occur every 50 years in response to a prescribed cyclic change in bed friction. Medial moraine patterns are simulated by tracking the englacial and supraglacial trajectory of debris injected at fixed points in the accumulation region. Stratification is assumed to be associated with isochronal surfaces, and vertical foliation is explained in terms of horizontal flattening of strain ellipsoids. Crevasses form when and where the intensity of tensile stress exceeds a prescribed threshold; crack damage is cumulative so that crevasse traces observed at sampling sites are a superposition of the damage accumulated en route. Folding is parameterized but not resolved. By evaluating the deformation gradient tensor along ice particle trajectories and applying the polar decomposition to this tensor, we isolate the cumulative effects of rotation and stretching by ice flow and calculate strain ellipsoids as well as other practical indicators of deformation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".