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Record W2013963050 · doi:10.1088/1748-9326/6/4/045204

Changes in snow cover characteristics over Northern Eurasia since 1966

2011· article· en· W2013963050 on OpenAlexaboutno aff
Olga Bulygina, Pavel Groisman, V. N. Razuvaev, Natalia N. Korshunova

Bibliographic record

VenueEnvironmental Research Letters · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSnowSnow fieldNorthern HemisphereWater equivalentClimatologySnow lineEnvironmental scienceClimate changeSnow coverPhysical geographyCurrent (fluid)GeologyGeographyMeteorologyOceanography

Abstract

fetched live from OpenAlex

Current snow state descriptions and estimates of major snow characteristics (snow cover duration, maximum winter snow depth, snow water equivalent) up to 2010 have been recorded from 958 meteorological stations in Russia. Apart from the description of long-term averages of snow characteristics, the estimates of their change that are averaged over quasi-homogeneous climatic regions are derived and regional differences in the change of snow characteristics are studied. In recent decades, the Russian territory has experienced an increase in snow depth, both winter average and maximum snow depths, against the background of global temperature rise and sea ice reduction in the northern hemisphere. The first generalized regional characteristics of maximum snow water equivalent in the winter season have been obtained. According to field observations, an increase in water supply has been revealed in the north of the East European Plain, in the western part by 4.5% (10 yr) − 1 and in the eastern part by 6 % (10 yr) − 1 . This characteristic also increases by ∼ 6 % (10 yr) − 1 in the southern forest zone of Western Siberia and in the Far East. Snow water equivalent in central Eastern Siberia increases by 3.4% (10 yr) − 1 . From snow course observations in the forest, a tendency for a decrease in water supply (−6.4 % (10 yr) − 1 is only found in the southwest of the East European Plain. Snow cover characteristics, being a product of several climate-forming factors that simultaneously affected them, change nonlinearly and different characteristics may and often do change differently with time. Therefore, one cannot assume that having information about the trend of one of the snow characteristics implies knowledge of the trend sign of others. In particular, whilst during the past four decades over the Russian Federation most snow cover characteristics—including the most important of them responsible for water supply—have increased, the only quantity that is reliably monitored from space (snow cover extent) has decreased, but in the last two decades this decrease has ceased. These tendencies are opposite to those observed in Canada and Alaska.

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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.250
Teacher spread0.196 · 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

Citations184
Published2011
Admission routes1
Has abstractyes

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