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Record W2052415674 · doi:10.1007/s10546-011-9639-8

Low-Level Jets in the Moscow Region in Summer and Winter Observed with a Sodar Network

2011· article· en· W2052415674 on OpenAlexfundno aff
M. A. Kallistratova, Rostislav Kouznetsov

Bibliographic record

VenueBoundary-Layer Meteorology · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
FundersInstitute of Aboriginal Peoples HealthRussian Foundation for Basic Research
KeywordsSODARDiurnal cycleNocturnalDiurnal temperature variationEnvironmental scienceDoppler effectMeteorologyAtmospheric sciencesPlanetary boundary layerClimatologyGeologyGeographyPhysics

Abstract

fetched live from OpenAlex

We evaluate the statistical properties of low-level jets (LLJs) observed by means of a network of Doppler sodars in the Moscow region, Russia. Continuous long-term measurements of the echo-signal intensity and wind-velocity profiles were carried out in July 2005 and in 2008–2010 synchronously in the centre of Moscow and at a rural site. The summertime nocturnal LLJs have a very clear diurnal cycle and exhibit features predicted by the Blackadar mechanism. In contrast, the long-lasting wintertime jets do not have any clear diurnal variability. The urban environment strongly influences LLJs in both seasons: above the city LLJs are higher, weaker and observed more rarely than at the rural site. In very cold periods (air temperature below −8°C) no LLJs were observed over the city, instead convection emerged in the urban boundary layer. The results are based on observations made in July 2005, January and December 2009, and January 2011.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.134
GPT teacher head0.247
Teacher spread0.113 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2011
Admission routes1
Has abstractyes

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