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Record W3156575611 · doi:10.1016/j.accre.2021.03.014

Permafrost dynamics and their hydrologic impacts over the Russian Arctic drainage basin

2021· article· en· W3156575611 on OpenAlexaff
Kang Wang, Tingjun Zhang, Daqing Yang

Bibliographic record

VenueAdvances in Climate Change Research · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsEnvironment and Climate Change Canada
FundersDivision of Earth SciencesNational Key Research and Development Program of ChinaMinistry of Science and Technology of the People's Republic of ChinaChinese Academy of SciencesNational Science Foundation
KeywordsPermafrostSurface runoffDrainage basinArcticActive layerStructural basinHydrology (agriculture)Environmental scienceDrainagePrecipitationGeologyOceanographyGeomorphologyLayer (electronics)EcologyGeography

Abstract

fetched live from OpenAlex

Permafrost is an important component in hydrological processes because changes in runoff over the Arctic drainage basin cannot be well explained by changes in precipitation-related variables. However, current understanding of the influences of permafrost on hydrological dynamics is insufficient. This study investigated historical variations in permafrost conditions and their potential hydrologic effects over the Russian Arctic drainage basin. The results show that soil temperature (at 0.40 m below surface) has increased about 1.4 °C over the Ob, 1.5 °C over the Yenisei, and 1.8 °C over the Lena River basin from 1936 through 2013, possibly resulted in a significant thawing of permafrost. Rapid active layer changes have occurred since the 1970s. The volume of the active layer increased by 28, 142, and 228 km3 over the Ob, Yenisei, and Lena basins, respectively, since the 1970s. Melting ground ice caused by deepening active layer may be a limited contribution to annual runoff. Runoff during freeze season (October–April) showed significant positive correlations (p < 0.05) to active layer thickness in the Yenisei and Lena basins while negative correlation (p > 0.05) in the Ob basin. These results imply that, in basins with high permafrost coverage, a deeper active layer increased soil water storage capacity and perhaps contribute to an increase in winter runoff.

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)0.002
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 score0.191
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.356
Teacher spread0.265 · 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

Citations46
Published2021
Admission routes1
Has abstractyes

Explore more

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