Spatial Temporal Compensation for Sea Ice Classification From GNSS-R Data
Bibliographic record
Abstract
This study proposes the use of a per-track normalization scheme and ancillary temporal and temperature data to improve the performance of global navigation satellite system reflectometry (GNSS-R) based sea ice classification. GNSS-R data and sea ice type labels were provided by TechDemoSat-1 (TDS-1) and National Snow and Ice Data Center (NSIDC) datasets, respectively. The delay Doppler maps (DDMs) from TDS-1 were used to compute DDM average (DDMA) observable values to which the proposed per-track normalization scheme was applied. The transformed observables with ancillary time and temperature data were provided to a random forest (RF) machine learning classifier to perform the final sea ice classifications. The purpose of this proposed method was to standardize power measurements across different TDS-1 tracks and to compensate for seasonal changes in sea ice that complicate sea ice classification. When using TDS-1 data within the temporal and geographic scope of this study, the method reported a testing accuracy of 84.86% and per-class F1 scores of 90.11%, 64.20%, and 81.59% for first-year ice (FYI), multi-year ice (MYI), and thin ice (TI), respectively. The proposed method was compared with existing methods of GNSS-R based sea ice classification and demonstrated improved or comparable performance. The proposed method shows promise, as it provides good performance while demonstrating generalizability to geographic and temporal data selection, and requires less data to train to a level comparable to established methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".