Stratospheric Smoke With Unprecedentedly High Backscatter Observed by Lidars Above Southern France
Bibliographic record
Abstract
Abstract Extreme pyroconvection events triggered by wildfires in northwest Canada and United States during August 2017 resulted in vast injection of combustion products into the stratosphere. The plumes of stratospheric smoke were observed by lidars at Observatoire de Haute‐Provence (OHP) for many weeks that followed the fires as distinct aerosol layers with backscatter reaching unprecedentedly high values for a nonvolcanic aerosol layer. We use spaceborne CALIOP lidar to track the spatiotemporal evolution of the smoke plumes before their detection at OHP. A remarkable agreement between ground‐ and spaced‐based lidars sampling the same smoke plume on a particular date allowed us to extrapolate the OHP observations to a regional scale, where CALIOP reported extreme aerosol optical depth values as high as 0.21. On a monthly time scale, the lidar observations indicate that boreal summer 2017 forest fires had a hemisphere‐scale impact on stratospheric aerosol load, similar to that of moderate volcanic eruptions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".