Mapping Sea Ice Concentration in the Canadian Arctic with CryoSat-2
Bibliographic record
Abstract
Abstract. Sea ice concentration (SIC) is an essential parameter for understanding environmental change in the polar regions. Historically, SIC has been determined using satellite passive microwave (PMV) radiometry, and this has revealed a progressive decline in the extent of the ice cover in the Arctic since records began in 1979. At regional and local scale, classifications based on satellite radar and optical imagery are practical. Here, we use CryoSat-2 to derive a new SIC product in the Canadian Arctic (CA), a region that is vital for shipping, freshwater production, and multi-year ice transport but is frequently excluded from pan-Arctic sea ice satellite observations. The 300 m along-track sampling of CryoSat-2 allows the fine-scale distribution of sea ice to be resolved, and an empirical correction for the overestimation of leads and misclassification of floes allows SIC to be determined. In general, spatial and temporal variations in SIC determined from CryoSat-2 are in close agreement with those determined from PMV and synthetic aperture radar (SAR) imagery in ice charts. Across the CA region, the root mean square difference (RMSD) between SIC determined monthly from CryoSat-2 and PMV and weekly from ice charts are 8.4 and 10 %, respectively. A local comparison to SIC determined from 82 cloud-free Landsat 8 scenes acquired in the central CA shows an RMSD of 3.3 %. Our findings highlight the complementarity of SIC records determined from CryoSat-2 and their potential to expand our knowledge of ice conditions in the CA.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".