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Record W3114176309 · doi:10.14430/arctic71674

Climate-Change Induced Permafrost Degradation in Yakutia, East Siberia

2020· article· en· W3114176309 on OpenAlexvenueno aff
Jolanta Czerniawska, Jiřı́ Chlachula

Bibliographic record

VenueARCTIC · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersNortheast Forestry University
KeywordsPermafrostClimate changeThermokarstMeltwaterEnvironmental sciencePhysical geographyPrecipitationTaigaSnowmeltClimatologyGlobal warmingLoggingSnowHydrology (agriculture)GeologyGeographyGlacierGeomorphologyOceanography

Abstract

fetched live from OpenAlex

Current climate change in the northern regions is a well-recognized phenomenon. In central Yakutia (the Sakha Republic), the long-term trend displays a consistent mean annual air temperature (MAAT) increase from −9.6˚C (1980) to −6.7˚C (2019), corresponding to an average 0.07˚C annual rise, with pronounced temperature anomalies in the last decade. The analyzed meteorological records of the past 40 years indicate a progressing climate change pattern of increased MAAT and mean annual precipitation (MAP) that occurs in 5 – 7 yr cycles. The complex interactions of regional climatic variations with local geological and environmental conditions influence the frozen ground’s thermal balance, which, in turn, impacts thermokarst development. Co-acting factors of temperature rise and higher precipitation rates activate thermokarst lake dynamics and lake expansion following snow- and rainfall-rich preceding years. April experiences the greatest warming trend with a present (2020) 5˚C rise from 1980 with shortening of the winter season. Climate warming together with natural forest fires and anthropogenic activities (pastoral practices and logging) contribute to the taiga landscape opening due to reduced albedo and the greater exposure to solar radiation. The regional hydrologic network undergoes restructuring caused by drained meltwater released from the degraded cryolithozone with peaks of the fluvial discharge in late spring and early summer generating bank erosion. The negative effects of the progressing ground thaw, which are particularly observed in lowland locations, pose risks to local settlements and generate major environmental and engineering problems in the formerly permafrost-stable central and northern areas of Siberia.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.999

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)0.0080.002

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 head0.114
GPT teacher head0.255
Teacher spread0.141 · 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 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

Citations32
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueARCTICSame topicClimate change and permafrostFrench-language works237,207