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Record W2916834803 · doi:10.3103/s1068373916020102

Ozone Content over the Russian Federation in 2015

2016· article· en· W2916834803 on OpenAlexaboutno aff
A. M. Zvyagintsev, N. S. Ivanova, G. M. Kruchenitskii, И. Н. Кузнецова, V. A. Lapchenko

Bibliographic record

VenueRussian Meteorology and Hydrology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsObservatoryRussian federationEnvironmental scienceMeteorologySatelliteQuarter (Canadian coin)OzoneRemote sensingGeographyPhysicsRegional scienceAstronomy

Abstract

fetched live from OpenAlex

The review is compiled on the basis of the results provided by the system of total ozone (TO) monitoring in the CIS and Baltic countries that functions in the operational regime at the Central Aerological Observatory (CAO). The monitoring system uses data from the national network of M-124 filter ozonometers being under the methodological supervision ofthe Main Geophysical Observatory. The functioning of the entire system is under operational control based on the observations obtained from the OMI satellite equipment (NASA, USA). Basic TO observation data are generalized for each month of the fourth quarter of 2015, for the fourth quarter, and for the year as a whole. The results of regul ar observations of surface ozone content carried out in the Moscow region and Crimea are also provided.

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 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 score0.043
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.225
Teacher spread0.209 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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