Design requirements for the SWIFT instrument
Bibliographic record
Abstract
The Stratospheric Wind Interferometer for Transport studies (SWIFT) instrument is a proposed limb-viewing satellite instrument that employs the method of Doppler Michelson interferometry to measure stratospheric wind velocities and ozone densities in the altitude range of 15–45 km. The values of the main instrument parameters including filter system parameters and Michelson interferometer parameters are derived using simulations and analyses. The system design requirements for the instrument and spacecraft are presented and discussed. Some of the retrieval-imposed design requirements are also discussed. Critical design issues are identified. The design optimization process is described. The sensitivity of wind measurements to instrument characteristics is investigated including the impact on critical design issues. Using sensitivity analyses, the instrument parameters were iteratively optimized in order to meet the science objectives. It is shown that wind measurements are sensitive to the thermal sensitivity of the instrument components, especially the narrow filter and the Michelson interferometer. The optimized values of the main system parameters including Michelson interferometer optical path difference, instrument visibility, instrument responsivity and knowledge of spacecraft velocity are reported. This work also shows that the filter thermal drift and the Michelson thermal drift are two main technical risks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.009 | 0.004 |
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".