Study of the precision of upper atmospheric wind field measurement
Bibliographic record
Abstract
The passive optical methods to observe the earthly upper atmospheric wind field by satellite remote sensing is to measure the parameters including atmospheric wind velocities, temperature, pressure and volume emission rates of airglow (aurora). WINDII is the first image interferometer for upper atmospheric wind measurement in 1991 made by Canada and France loaded on NASA's UARS. The precision of wind speed is 10m/s for WINDII and its temperature precision is 10K. The second wind measurement instrument of SWIFT is launched at 2011 based on the same principle as WINDII. SWIFT's wind speed precision is 3m/s, and its temperature precision is 2K. According to the development of the photoelectron technology and CCD, the wind field's detected precision is enhanced continuously. In this paper, the theory of detected precision of wind speed and temperature is analyzed firstly; the factors between the higher precision of wind field and CCD detector parameter are made sure. And then the precision equation is deduced. The wind speed and temperature precision expression includes of optical path difference (OPD), phase, aurora wavelength, visibility, CCD's responsibility, signal-to-noise, view of field (VOF) etc. The precision of 1m/s wind speed and 1K temperature need fixed OPD 24.28cm with O+ 732.0nm aurora. This research can provide the theory for advance upper atmospheric wind field detecting precision.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".