A Schematic of Track-wisely Calibrating CyGNSS Data
Bibliographic record
Abstract
Cyclone Global Navigation Satellite System (CyGNSS) can provide intermediate geophysical data, e.g., surface reflectivity $(\Gamma)$, to facilitate various land remote sensing applications. The reliability of $\Gamma$ depends on the precision of the effective instantaneous radiative power (EIRP) of the transmitted GNSS signals in the forward scattering direction. However, it is challenging to completely obtain accurate EIRP estimates. In order to mitigate such impact, this paper develops an effective scheme for calibrating the CyGNSS $\Gamma$ data in a track-wise fashion over land. Here, ten or more consecutive CyGNSS measurements outside the scope of annual max/min are to be calibrated, and the corresponding monthly medians are selected as references. The resulting $\Gamma$ products are assessed by direct visual inspection which demonstrates the effectiveness of the proposed schemes with immediate removal/fix of track-wisely noisy data. This work provides an effective and robust way to calibrate the CyGNSS $\Gamma$ result, which will further improve relevant remote sensing applications in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.024 | 0.011 |
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".