Stratospheric Smoke With Unprecedentedly High Backscatter Observed by Lidars Above Southern France
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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 it