Sea ice kinematics and surface properties from RADARSAT synthetic aperture radar during the SHEBA drift
Bibliographic record
Abstract
Satellite data are important for providing the large‐scale context of the Surface Heat Budget of the Arctic Ocean (SHEBA) station and for characterizing the spatial variability of the sea ice in its vicinity. The Canadian RADARSAT satellite collected 195 synthetic aperture radar (SAR) images of the SHEBA site over the course of the 1 year drift. The RADARSAT Geophysical Processor System (RGPS) used these images to compute the spatial pattern of ice motion within 100 km of the SHEBA station by tracking features in sequential images. From the ice motion data the divergence and shear of the pack ice are estimated. The divergence is large from November to January, followed by a gradual convergence from February through July. The character of the ice motion changes at the end of July, from piecewise rigid motion to free drift. The ice motion reverts to its winterlike character in late September. Thus the “kinematic” summer runs from late July to late September. The radar backscatter also goes through seasonal transitions, capturing the abrupt onset of melt (29 May) and freeze‐up (15 August). The concentration of multiyear ice is about 94% in the fall, and its backscatter signature remains stable through spring. Multiyear and first‐year ice cannot be distinguished during the summer melt season, when the mean backscatter is negatively correlated with the surface air temperature. The “thermodynamic” summer runs from late May to mid‐August.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".