Retrieval of ionospheric TEC over oceans From GNSS-R delay-Doppler map
Bibliographic record
Abstract
In this paper, an approach is presented to retrieve ionospheric total electron content (TEC) over oceans from Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler map (DDM). Here, an additional ionospheric delay (τI) is considered in the conventional DDM simulation process. Based on this, the least squares (LS) fitting method is employed for TEC retrieval by fitting the simulated DDMs with different τI values to the measured DDM. Meanwhile, an adaptive threshold is adopted in the fitting process to restrain the intrinsic errors due to DDM mismatching. The proposed method is validated by comparing the retrieved TEC results based on three datasets from the SSTL UK-DMC satellite with the TEC values produced by the International Reference Ionosphere (IRI)-2012 and the NeQuick 2 models. A good consistency is obtained.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 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".