Stratospheric Ozone Content Variations Over the City of Obninsk from Data of Lidar and Satellite Measurements
Bibliographic record
Abstract
Abstract An analysis of variations in the height-integrated stratospheric ozone content in layers of 13–18, 18–23, and 23–30 km according to the data of lidar and satellite measurements in 2014–2022 over the city of Obninsk (55.1° N, 36.6° E) is presented. The simulation of interannual ozone variations for individual quarters of the year is carried out using the method of multiple linear regression. Quasi-biennial oscillations (QBO) of the equatorial wind, Arctic oscillation (AO), El Niño Southern Oscillation (ENSO), solar activity (SA), volcanic aerosol (VA), and polar stratospheric clouds (PSC) are considered as influencing factors. An increase in the ozone content is observed in the eastern phase of QBO in the altitude range of 18–30 km (I–II quarter) and in the western phase of QBO within the interval of 13–23 km (IV quarter). In separate layers, significant influences of AO (II–III quarters), SA (I–II quarter), and VA (III–IV quarters) are found. The PSC influence during the year manifests itself first in II quarter in the layer of 13–18 km, and then in IV quarter in the layer of 13–23 km. Possible physical mechanisms underlying the observed correlations are considered.
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".