Measurements of characteristics of stratospheric aerosol layer at Siberian lidar station in Tomsk
Bibliographic record
Abstract
In this report we present an analysis of lidar measurements of aerosol optical characteristics in the stratosphere over Tomsk, based on which we determined the periods of increased aerosol content after anomalous aerosol layers of nonvolcanic origin from severe forest fires in Canada were recorded in summer-fall period of 2017. Owing to the pyroconvection, the fire products were lifted to the stratosphere and spread over the entire Northern Hemisphere. The thickness of the recorded aerosol layers of non-volcanic origin was comparable to that produced by one of volcanoes in Pacific Ring of Fire in 2006-2012. A background state of aerosol loading has been established in the stratosphere over Tomsk since November 2017. Time series of long-term measurements of integrated aerosol backscattering coefficient Вaπ for the background stratospheric state from 2012 to July 2017 was used to construct a linear regression of the form Вaπ=2.2213•10−4−1.7936•10-7•t. Our regional empirical model of background stratospheric aerosol for 2000-2016 was corrected to include the measurements from 2016 to 2019.
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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) | 0.000 | 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".