Multi-year and first-year ice from RCM for assimilation in ECCC ice type analysis system
Bibliographic record
Abstract
Arctic sea ice type information is essential for various operational and scientific applications including the support of marine users and guiding ice thickness retrieval algorithms operating with SMOS and CryoSat-2 data for improved sea ice prediction. A sea ice type analysis system developed at Environment and Climate Change Canada’s (ECCC) generates pan-Arctic ice type analyses at 5 km resolution every 6 hours. The ice type analysis system assimilates ice type information provided by passive microwave (AMSR2, SSMIS) and scatterometer (ASCAT) data, but assimilation of these observations is not reliable in the areas near land and in the narrow channels of the Canadian Arctic Archipelago due to their low spatial resolution of ~20-50 km. Therefore, assimilation of high-resolution ice type observations from synthetic aperture radar (SAR) is highly desired.In this study, we extended our approach for automated detection of winter multi-year ice (MYI) and first-year ice (FYI) at 1.6 km scale from RADARSAT-2 to RCM data. To this end, we collected more than 2,000 RCM images and corresponding image analyses products that were manually generated by the Canadian Ice Service (CIS) ice experts for the period of time between July 1, 2020 and July 31, 2023. From these RCM images we extracted SAR information for more than 30,000 pure MYI and more than 619,000 pure FYI data samples under no melt conditions as identified by the CIS image analyses.We demonstrated that separability measures for MYI and FYI classes in the spaces of the two predictor parameters (HV/HH polarization ratio and standard deviation of HV signal) are consistent with those we previously observed for RADARSAT-2. Furthermore, we found that our RCM-based MYI/FYI detection approach allows us to classify 60% of the winter CIS ice data samples with the accuracy of 99.6%. Preliminary results of assimilating RCM-based MYI/FYI high-resolution retrievals in combination with passive microwave and scatterometer data in the ECCC ice type analysis system will be also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".