First Multiclass Arctic Sea Ice Classification Results From FY-3E Data
Bibliographic record
Abstract
This study performs the first multiclass sea ice classification using global navigation satellite system (GNSS) reflectometry (GNSS-R) data from the Fengyun-3E (FY-3E) satellite. A principal component analysis (PCA) based denoising scheme for GNSS-R delay Doppler maps (DDMs) is proposed. The newly proposed processing methods are applied to FY-3E satellite tracks to retain the most significant features of the data. After removing noise from the DDMs using PCA, DDM classification features are calculated using methods established in the literature. An extremely randomized trees (ET) machine learning (ML) classifier was used to classify the sea ice using the denoised classification observables. The classification results are validated using labels derived from National Snow and Ice Data Center (NSIDC) sea ice concentration data. The output classification accuracy was 87.14% with a 0.7834 Kappa coefficient. Per class F1 scores were 89.72%, 85.53%, and 81.34% for first-year ice (FYI), multi-year ice (MYI), and thin ice (TI), respectively. The proposed method produced improvements across all performance metrics compared to classification without PCA based observables and denoising.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".