Data fusion analysis of Sea Surface Temperature from HY-2A satellite radiometer
Bibliographic record
Abstract
The global sea surface is not seamlessly covered by the along-track sea surface temperature (SST) data of the scanning microwave radiometer (SMR) aboard the Haiyang-2A (HY-2A) satellite, the first ocean dynamic environment satellite of China. In this paper, this coverage problem was solved by data fusion. The whole procedure includes the following steps. First, SST data within 200km of the coastline were removed, and so were outliers. Second, the HY-2A SST data were gridded, filtered and corrected. Third, the inverse distance weighted (IDW) method was used to merge the HY-2A SST data into those from an operational, high-resolution, combined sea surface temperature and sea ice analysis (OSTIA) system. The Global 1-km Sea Surface Temperature (G1SST) data were used as the reference data. The root mean square (RMS) difference between the along-track SST data of HY-2A and the G1SST SST data is 1.3°C. The HY-2A SST data were about 83% left and the RMS is 0.7°C after preprocessing. The along-track SST data of HY-2A can be well used for data fusion. The RMS for the fusion results is 0.6°C, and the gaps between tracks were filled up.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".