Influence of high-order mechanics on simulation of glacier response to climate change: insights from Haig Glacier, Canadian Rocky Mountains
Bibliographic record
Abstract
Abstract. Evolution of glaciers in response to climate change has mostly been simulated using simplified dynamical models. Because these models do not account for the influence of high-order physics, corresponding results may exhibit some biases. For Haig Glacier in the Canadian Rocky Mountains, we test this hypothesis by comparing simulation results obtained from 3-D numerical models that deal with different assumptions concerning ice-flow physics, ranging from simple shear-deformation to comprehensive Stokes flow. In glacier retreat scenarios, we find a minimal role of high-order mechanics in glacier evolution, as geometric effects at our site (the presence of an overdeepened bed) result in limited horizontal movement of ice (flow speed on the order of a few meters per year). Consequently, high-order and reduced models all predict that Haig Glacier ceases to exist by ca. 2080 under ongoing climate warming. The influence of high-order mechanics is evident, however, in glacier advance scenarios, where ice speeds are greater and ice dynamical effects become more important. To generalize these findings for other glacier applications, we advise that high-order mechanics are important and therefore should be considered while modelling the evolution of active glaciers. Reduced model predictions may, however, be adequate for other glaciologic and topographic settings, particularly where flow speeds are low.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".