A Hybrid CNN-Transformer Network for Global Ocean Wind Speed Retrieval from GNSS-R Data
Bibliographic record
Abstract
Global navigation satellite system reflectometry (GNSS-R) has emerged as a promising technique for global sea surface wind speed measurement. Currently, a lot of methods based on convolutional neural networks (CNNs) have been developed to extract features from delay Doppler maps (DDMs) and retrieve wind speed from GNSS-R data. However, CNNs emphasize local features and cannot capture global features due to their inherent locality. In order to address this limitation, a hybrid CNN-Transformer Network (CTN) is proposed in this study. Specifically, the hybrid CTN consists of a CNN branch tailored for capturing local features and a Transformer branch for global features. By combining these distinct features, the designed hybrid CTN facilitates a comprehensive representation of DDMs. Experiments conducted on Cyclone GNSS (CYGNSS) data demonstrate improved accuracy and robustness of the proposed method in wind speed retrieval from GNSS-R data.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".