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OZONE CONTENT OVER THE RUSSIAN FEDERATION IN THE FIRST QUARTER OF 2022

2022· article· en· W4312539335 on OpenAlexaboutno aff
N.S. IVANOVA, I.N. KUZNETSOVA, E.A. LEZINA

Bibliographic record

VenueMeteorologiya i Gidrologiya · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsObservatoryQuarter (Canadian coin)MeteorologyEnvironmental scienceOzoneRussian federationSatelliteObservational studyRemote sensingGeographyEngineeringRegional sciencePhysicsAstronomyAerospace engineering

Abstract

fetched live from OpenAlex

The review is based on the results of operation of the total ozone (TO) monitoring system over the CIS and Baltic countries, operating in the operational mode at the Central Aerological Observatory (CAO). The monitoring system uses data from the domestic network of M-124 filter ozonometers, operating under the methodological guidance of the Main Geophysical Observatory; the quality of the entire system is operatively controlled in the Central Administrative District by comparison with the observational data made with the help of the OMI satellite equipment (NASA, USA). The main TO observational data for each month of the first quarter of 2022 and for the quarter as a whole are summarized. The results of regular measurements of ground-level ozone carried out in the Moscow region are also summarized.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.022
GPT teacher head0.211
Teacher spread0.190 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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