Temporal evolution of nightglow emission responses to SSW events observed by TIMED/SABER
Bibliographic record
Abstract
[1] Using the SSW (Stratospheric Sudden Warming) event in 2009 as a representative case, the temporal evolution of the responses of OH and O2 infrared atmospheric (0–0) nightglow emissions to SSW events is analyzed using the TIMED (Thermosphere Ionosphere Mesosphere Energetics and Dynamics)/SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) data. The results show that during the mesospheric cooling that occurs during the stratospheric warming stage of SSW events, the brightness of OH and O2 nightglow emissions and the thicknesses of OH and O2 emission layers decrease noticeably and the peak heights of the emissions ascend. During the recovery stage in the mesosphere, the brightness of both nightglow emissions and the thicknesses of the emission layers increase dramatically and the peak heights of the emissions descend. These emission variations are mainly caused by perturbations in temperature and the transport of O in the MLT (Mesosphere Lower Thermosphere) region. For the SSW event that started in January 2009, the onset times of the cooling stage and recovery stage in the mesosphere are ∼2 days ahead of the onset times of the warming stage and recovery stage of the SSW event, respectively. For this event, the influence of the SSW on the OH and O2 nightglow emissions increases with latitude between 50°N and 80°N.
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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) | 0.001 | 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".