A sea ice deformation and rotation rate dataset (2017–2023) from the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS)
Bibliographic record
Abstract
Abstract. Sea ice forms a thin but horizontally extensive boundary between the ocean and the atmosphere and has complex, crust-like dynamics characterized by intermittent sea ice deformations. The heterogeneity and localization of these sea ice deformations are important characteristics of the sea ice cover that can be used to evaluate the performance of dynamical sea ice models against observations across multiple spatial and temporal scales. Here, we present a new pan-Arctic sea ice deformation and rotation rate (SIDRR; https://doi.org/10.5281/zenodo.13936609, Plante et al., 2024a) dataset derived from the RADARSAT Constellation Mission (RCM) and Sentinel-1 (S1) synthetic aperture radar (SAR) imagery from 1 September 2017 to 31 August 2023. The SIDRR estimates are derived from contour integrals of triangulated ice motion data, obtained from the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS). The SIDRR dataset is not regularized and consists of stacked data from multiple SAR images computed on a range of spatial (4–10 km) and temporal (0.5–6 d) scales. It covers the entire Arctic Ocean and all peripheral seas except the Okhotsk Sea. Uncertainties associated with the propagation of tracking errors on the deformation values are included. We show that rectangular patterns of deformation features are visible when the sampled deformation rates are lower than the propagation error. This limits the meaningful information that can be extracted in areas with low SIDRR values but allows for the study of linear kinematic features with a high SIDRR signal-to-noise ratio. The spatial coverage and range of resolutions of the SIDRR dataset provide an interesting opportunity to investigate regional and seasonal variability in sea ice deformation statistics across scales, and these data can also be used to determine metrics for model evaluation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".