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Record W4387941275 · doi:10.1134/s0001433823050079

Stratospheric Ozone Content Variations Over the City of Obninsk from Data of Lidar and Satellite Measurements

2023· article· en· W4387941275 on OpenAlexaboutno aff
V. A. Korshunov

Bibliographic record

VenueIzvestiya Atmospheric and Oceanic Physics · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceAtmospheric sciencesOzoneSatelliteStratosphereLidarOzone layerQuasi-biennial oscillationQuarter (Canadian coin)Altitude (triangle)ClimatologyAerosolMeteorologyGeographyGeologyPhysicsRemote sensing

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.256
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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