State of the Stratospheric Aerosol Layer over Tomsk in 2017 Using Data from Sensing at Siberian Lidar Station in Tomsk
Bibliographic record
Abstract
In this study, we present the observations of anomalous aerosol layers in summer-fall period of 2017; the observations were performed at the Siberian Lidar Station in Institute of Atmospheric Optics, Siberian Branch, Russian Academy of Sciences, at two wavelengths (355 and 532 nm). At the layer maximum, we recorded a narrow layer ~1 km in altitudinal extents on August 26, 2017 with the scattering ratios R355 = 2.8 and R532 = 5.8 at the altitude of 15 km. On subsequent days, the layers spanned a wider altitude interval, but were characterized by smaller scattering ratios. The availability of results from sensing these layers at two wavelengths, and accounting for the lidar ratios on the basis of model values, allowed us to estimate the Ångström exponent (X) both in these layers, and at the altitudes that remained undisturbed, i.e. at a background aerosol state. The minimal Ångström exponent is unity or larger in well-defined anomalous layers; while for the background aerosol, localized above 16 km, the Ångström exponent is in the interval (X = 2.8–3.8), with a pronounced positive gradient with the growing altitude. The constructed back trajectories of air mass motion showed that the source of aerosol layers in the stratosphere over Tomsk had been forest fires in North America (Canada) in the mid-August 2017.
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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".