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Record W2731721016

Температурные контрасты осенних месяцев 2014 года в России

2014· article· ru· W2731721016 on OpenAlexaboutno aff
Владимир Леонидович Сывороткин

Bibliographic record

VenueПространство и Время · 2014
Typearticle
Languageru
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAnticycloneAnomaly (physics)Atmosphere (unit)Atmospheric sciencesClimatologyOzoneTroposphereOzone layerEnvironmental scienceGeologyStratosphereGeographyMeteorologyPhysics
DOInot available

Abstract

fetched live from OpenAlex

The main cause of the weather (and climate) anomalies is the fluctuations of total ozone in the atmosphere. These fluctuations are caused by (i) emission of the deep ozone-depleting gases (hydrogen and methane), and (ii) variations of the geomagnetic field, which increase the concentration of ozone. The positive ozone anomalies cool the troposphere and create anti-cyclones, dry, heavy and slow moving air masses. The negative anomalies warm up the air and create the cyclonic masses with law pressure. The closest anticyclones could move to that area bringing with them the anomalous temperatures, sometimes very high and sometimes very law. In my subheading, I analyze temperature anomalies of autumn 2014 in These anomalies were about ten-day duration and alternated. So, starting in September, the anomalous heat rhythmically was replaced by abnormal cold, which in the end of November reached values typical for January. The causes of the temperature anomalies were synchronous total ozone anomalies. Stable ozone hole in the northern part of European Russia played the main role in this process. Southern anticyclones, which bringing the anomalous heat, was periodically drawn into this hole. In November, the anticyclone, which was formed in Siberia and which was brought the ‘January’ frosts in European Russia, even in its southern provinces, was also drawn into in this hole.  weather (climate) anomaly; anomalies of ozone; ozone layer; deep degassing; hydrogen; snowfalls; frosts About Successes of the Russian Winter. GISMETEO News. N.p., 6 Nov. 2014. Web. . (In Russian). Due to Ыnowfall, There Were 641 Accidents per 48 Hours in the Perm Region. GISMETEO News. N.p., 20 Oct. 2014. Web. . (In Russian). Golubov B.N. Reflection on the Amazing Information the Geological, Environmental and Political Aspects of the Storage and Disposal of Nuclear Materials. Space and Time 2 (2012): 224–228. (In Russian). Hot September: Anomalies in Siberia, in Russia and in the World. Ermak-info. N.p., 1 Oct. 2014. Web. . (In Russian). Hydrometeorological Centre of Major Weather and Climatic Features in September 2014 in the Northern Hemisphere. Hydrometeorological Centre of Russia: About Weather – At First Hand. Federal Service for Hydrometeorology and Environmental Monitoring, n.d. Web. . (In Russian). Hydrometeorological Centre of Major Weather and Climatic Features of October 2014 in the Northern Hemisphere. Hydrometeorological Centre of Russia: About Weather – At First Hand. Federal Service for Hydrometeorology and Environmental Monitoring, n.d. Web. . (In Russian). In January, Frosts Came to Chernozemye. GISMETEO News. N.p., 26 Nov. 2014. Web . (In Russian). In Russia, the Abnormal Frosts Are Amplified. GISMETEO News. N.p., 25 Nov. 2014. Web. . Major Weather and Climatic Features in November 2014 in the Northern Hemisphere. Hydrometeorological Centre of Russia: About Weather – At First Hand. Federal Service for Hydrometeorology and Environmental Monitoring, n.d. Web. . (In Russian). November Ended with Anomalous Frosts. GISMETEO News. N.p., 30 Nov. 2014. Web. . (In Russian). Premature Snowfalls in the Perm Region Destroyed Harvest of Cabbage. GISMETEO News. N.p., 24 Nov. 2014. Web. . (In Russian). Select Ozone Maps. Ozone and Ultraviolet Research and Monitoring. Environment Canada's World Wide Web Site. The Green LaneTM. Web. . Syvorotkin V.L. Uselessness of the Montreal Protocol To Save the Earth's Ozone Layer. Space and Time 3 (2014): 256–265. (In Russian). There Is a Record Atmospheric Pressure in Moscow!. Phobos. N.p., 10 June 2014. Web. . (In Russian). There is Minus 50° C in Siberia!. GISMETEO News. 24.11 2014. Web. . (In Russian). Two Anomalies in November in Chernozemye. GISMETEO News. N.p., 1 Dec. 2014. Web . (In Russian). Weather in Russia Has Mixed Climate Zones. GISMETEO News. N.p., 27 Nov. 2014. Web. . (In Russian). Syvorotkin, V. L. Temperature Contrasts in the Autumn Months of 2014 in Russia. Space and Time 4 (2014): 222–229. (In Russian). Fixed network address 2226-7271provr_st4-18.2014.102.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.037

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 head0.004
GPT teacher head0.178
Teacher spread0.175 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2014
Admission routes1
Has abstractyes

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