MétaCan
Menu
Back to cohort
Record W2568137953 · doi:10.3390/w9010025

Increasing Winter Baseflow in Response to Permafrost Thaw and Precipitation Regime Shifts in Northeastern China

2017· article· en· W2568137953 on OpenAlexaff
Liangliang Duan, Xiuling Man, Barret L. Kurylyk, Tijiu Cai

Bibliographic record

VenueWater · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsPermafrostBaseflowEnvironmental scienceHydrology (agriculture)Subarctic climatePrecipitationContext (archaeology)GroundwaterArcticClimate changeWatershedSurface runoffPhysical geographyClimatologyStreamflowDrainage basinGeologyGeographyEcologyOceanography

Abstract

fetched live from OpenAlex

Rapid permafrost thaw and precipitation regime shifts are altering surface and subsurface hydrological processes in arctic and subarctic watersheds. Long-term data (40 years) from two large permafrost watersheds in northeastern China, the Tahe River and Duobukuer River watersheds, indicate that winter baseflows are characterized by significant positive trends of 1.7% and 2.5%·year−1, respectively. Winter baseflows exhibited statistically significant positive correlations with mean annual air temperature and the thawing index, an indicator of permafrost degradation, for both watersheds, as well as the increasing annual rainfall fraction of precipitation for the Duobukuer River watershed. Winter baseflows were characterized by a breakpoint in 1989, which lagged behind the mean annual air temperature breakpoint by only two years. The statistical analyses suggest that the increases in winter baseflow are likely related to enhanced groundwater storage and winter groundwater discharge caused by permafrost thaw and are potentially also due to an increase in the wet season rainfall. These hydrological trends are first apparent in marginal areas of permafrost distribution and are expected to shift northward towards formerly continuous permafrost regions in the context of future climate warming.

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)0.000
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 score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.025
GPT teacher head0.252
Teacher spread0.227 · 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

Citations63
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueWaterSame topicClimate change and permafrostFrench-language works237,207