Reassessing the Quality of Sea‐Ice Deformation Estimates Derived From the RADARSAT Geophysical Processor System and Its Impact on the Spatiotemporal Scaling Statistics
Bibliographic record
Abstract
Abstract We reassess the trajectory errors inherent to sea‐ice deformation estimates with a new propagation of uncertainty derivation and show that previous formulations applied to deformation estimates from the RADARSAT Geophysical Processor System (RGPS) are either too high due to incorrect assumptions or too low due to neglected terms in certain cases. We show that when the resulting signal‐to‐noise ratios are used to discriminate the deformation estimates based on their quality, as done for buoy records, the spatiotemporal scaling exponents for the mean total deformation rate increase, especially at smaller scale, such that a space‐time coupling of the scaling—which is otherwise absent—emerges from the RGPS deformation data set, in accord with previous analyses performed with buoy observations. We also show that the preprocessing method used to reduce the effects of irregular sampling of the Lagrangian deformation fields can significantly impact the value of the deformation statistics and could possibly explain part of previous discrepancies between deformation statistics obtained with buoy records and large‐scale synthetic aperture radar (SAR) imagery. Specifically, we show that spurious lines of deformation appear when interpolating RGPS trajectories that presenttemporal sampling inconsistencies. In the context of using observed sea‐ice deformation statistics to constrain and improve the performance of sea‐ice models, high confidence in the observed deformation field statistics is necessary. Using appropriate, well‐documented, methods to derive the set of statistics to be reproduced by models therefore becomes crucial.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".