Sea‐ice deformation rates from satellite measurements and in a model
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The deformation of sea ice is an important element of the Arctic climate system because of its influence on the ice‐thickness distribution and on the rates of ice production and melt. New data obtained from the Radarsat Geophysical Processor System (RGPS) using satellite synthetic aperture radar images of the ice offers an opportunity to compare observations of the ice deformation to estimates obtained from models. The RGPS tracks tens of thousands of points, spaced roughly at 10‐km intervals, for an entire season in a Lagrangian fashion. The deformation is computed from cells formed by the tracked points, typically at 3‐day intervals. We used a coupled ice/ocean model with ice‐thickness and enthalpy distributions that covers the entire Arctic Ocean with a 40‐km grid. Model‐only and model‐with‐data‐assimilation runs were analysed. The data assimilation runs were analysed in order to determine the validity of the comparison techniques and to find the comparisons under the best of circumstances, when many buoy measurements are available for assimilation. This step is necessary because the RGPS and model data differ in spatial and temporal sampling characteristics. The assimilated data included buoy motion and Special Sensor Microwave/Imager (SSM/I)‐derived ice motion. The Pacific half of the Arctic Basin was analysed for a 10‐month period in 1997 and 1998. Comparisons of ice velocity observations to the modelled velocities showed excellent agreement from the model‐with‐data‐assimilation run but poorer agreement for the model‐only run. At a scale of 320 km, the deformation from the data assimilation run was in modest agreement with the observations but where many buoys were available for assimilation the agreement was quite good. Both model runs showed poor agreement during summer. Comparisons of the deformation distribution functions suggest why the agreements were poor even though the velocity agreements were good. Decreasing the ice strength parameter in the model improved the deformation comparisons for the model‐only runs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it