Bibliographic record
Abstract
Abstract. As in any environmental system, modeling instabilities within the glacial system is a numerical challenge of potentially high real-world relevance. Differentiating between the impacts of physical system processes and numerical noise is not straightforward. Here we use an idealized North American geometry and climate representation (similar to the HEINO experiments, Calov et al., 2010) to examine the numerical sensitivity of ice stream surge cycling in glaciological models. Through sensitivity tests, we identify some numerical requirements for a robust model configuration for such contexts. To partly address model-specific dependencies, we use both the Glacial Systems Model (GSM) and Parallel Ice Sheet Model (PISM). We show that modeled surge characteristics are resolution-dependent though converging (decreasing differences between resolutions) at higher horizontal grid resolutions. Discrepancies between high and coarse horizontal grid resolutions can be reduced by incorporating a resolution-dependent basal temperature ramp for basal sliding thermal activation. Inclusion of a diffusive bed thermal model reduces the surge cycling ice volume change by ∼33 % as the additional heat storage dampens the change in basal temperature during surge events. The inclusion of basal hydrology, as well as a non-flat topography, leads to increased ice volume change during surge events (∼20 and 17 %, respectively). Therefore, these latter three components are essential if one is endeavoring to maximize physical fidelity in ice stream surge cycle modeling. An abrupt transition between hard bedrock and soft sediment, as in the HEINO experiments, leads to ice stream propagation along this boundary but is not the cause of the main surge events.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.010 |
| Insufficient payload (model declined to judge) | 0.340 | 0.245 |
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".