A 1.5‐D anisotropic sigma‐coordinate thermal stress model of landlocked sea ice in the <scp>C</scp>anadian <scp>A</scp>rctic <scp>A</scp>rchipelago
Bibliographic record
Abstract
Abstract We present a 1.5‐D thermal stress model that takes into account the effect of land confinement, which causes anisotropy in thermal stresses. To this end, we fix the total strain in the direction perpendicular to the coastline to its value at landlocked ice onset. This prevents thermal expansion in the direction perpendicular to the coastline and therefore induces larger thermal stresses in this direction. The simulated stresses best match the observations, when a Young's Modulus of 0.5 GPa and a relaxation time constant of 8 days are used. This simulation gives root‐mean‐square errors of 13.0 and 13.1 kPa (∼15%) in the major and minor principal stresses, respectively. The simulated anisotropic component of thermal stress also generally agrees with observations. The optimal Young's Modulus is in the low range of reported values in the literature, and the optimal relaxation time constant (8 days) is larger than the largest relaxation time constant reported in the literature (5 days). A series of experiments are done to examine the model sensitivity to vertical resolution, snow cover, and the parameterizations of Young's Modulus and viscous creep. Results show that a minimum of one and three layers in the snow and ice, respectively, is required to simulate the thermal stresses within 15% error of the value assessed with the higher‐resolution control simulation. This highlights the importance of resolving the internal snow and ice vertical temperature profile in order to properly model the thermal stresses of sea 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".