Bibliographic record
Abstract
Microwave Radiation Imager (MWRI) on-board FengYun-3D (FY-3D) meteorological satellite is a total power, 10 channel conic scan microwave imager. Observing land/ocean surface and atmosphere from 10GHz to 89GHz, V and H polarization. During the on-orbit test of FY-3D/MWRI, RFI effect was found in some area for 10GHz and 18GHz separately. Both calibration data, including warm target and cold target, and earth environment observing data was consider to cause the RFI noise. 1 month data was used to find out the error source, result show that most of the RFI noise comes from cold target. Different from other conic scan microwave imager on-orbit, such as AMSR2, SSMIS and GMI, FY-3D/MWRI using an end to end calibration mechanism. A big cold-target reflector (1 m in diameter) was used on the top of the platform. In some specific locations of the orbit, emission of different source was reflected by the cold-target reflector and then reflected by the main reflector to the receiver. Detail results show that for 10V channel and 10H channel, most of the RFI effect was found during the platform flying pass the west Europe (south of France), while for 18V channel and 18H channel, most of the RFI effect was found during the platform flying pass the west of North America (west part of Canada/US border). Compared with Geostationary satellite that in-orbit operation, we found that this result shows a good agreement with television transmission satellite, including location and operational frequency. Based on this result, a new filter algorithm was designed to do the correction. Result show that most of the RFI effect in the former area was corrected.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".