Effects of temperature dependence of reaction N<sub>2</sub>(A<sup>3</sup> <font face="Symbol"><b>S</b></font><b><sup>+</sup><sub>u</sub>)</b> <b>+ O on</b>greenline dayglow emission
Bibliographic record
Abstract
Abstract. The greenline dayglow emission profiles measured by the Wind Imaging Interferometer (WINDII) on the Upper Atmosphere Research Satellite (UARS) are modelled using recently proposed revisions to the temperature dependent rate coefficient of the reaction N2 (A3 S+u ) + O in the glow model. The volume emission rates of greenline dayglow emissions are calculated using the Hinteregger et al. (1981) and Tobiska (1991) solar flux models. It is found that the average modelled profiles obtained using the Hinteregger et al. (1981) solar flux model with the temperature dependent rate coefficient and a quantum yield of 0.36 for the reaction N2 (A3 S+u ) + O agree to within 8% of the observed profiles in the thermospheric peak region, which shows significant improvement over the earlier results (20% smaller than WINDII results) obtained using the temperature independent reaction rate coefficient. On the other hand, the average modelled profiles obtained with a temperature dependent rate coefficient in the Tobiska (1991) solar flux model are about 12% higher than the WINDII results, whereas with the temperature independent rate coefficient the results are about 10% smaller than the WINDII results in the thermospheric peak region. The present study reveals that the emission profiles obtained using the Hinteregger et al. (1981) solar flux model, along with the temperature dependent rate coefficient and a quantum yield of 0.36 for the reaction N2 (A3 S+u ) + O in glow model, reproduce the thermospheric emission peak as observed by WINDII, a capability which eluded earlier models. These findings support the newly discovered temperature dependence of the rate coefficient of re-action N2 (A3 S+u ) with O.Key words. Ionosphere (ionization mechanisms; modeling and forecasting; general or miscellaneous)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".