Space‐based constraints on the production of nitric oxide by lightning
Bibliographic record
Abstract
We interpret observations of trace‐gases from three satellite platforms to provide top‐down constraints on the production of NO by lightning. The space‐based observations are tropospheric NO2 columns from SCIAMACHY, tropospheric O3 columns from OMI and MLS, and upper tropospheric HNO3 from ACE‐FTS. A global chemical transport model (GEOS‐Chem) is used to identify locations and time periods in which lightning would be expected to dominate the trace gas observations. The satellite observations are sampled at those locations and time periods. All three observations exhibit a maximum in the tropical Atlantic region and a minimum in the tropical Pacific. This wave‐1 pattern is driven by injection of lightning NO into the upper troposphere over the tropical continents, followed by photochemical production of NO2, HNO3, and O3 during transport. Lightning produces a broad enhancement over the tropical Atlantic and Africa of 2–6 × 1014 molecules NO2 cm−2, 4 × 1017 molecules O3 cm−2 (15 Dobson Units), and 125 pptv of upper tropospheric HNO3. The lightning background is 25–50% weaker over the tropical Pacific. A global source of 6 ± 2 Tg N yr−1 from lightning in the model best represents the satellite observations of tropospheric NO2, O3, and HNO3.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".