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Record W2559182939 · doi:10.5539/enrr.v6n4p116

Does Vegetation Restoration Change Regional Ecohydrological Condition at the Loess Plateau in China?

2016· article· en· W2559182939 on OpenAlexvenueno aff
Xiongwen Chen, Jianzhi Niu

Bibliographic record

VenueEnvironment and Natural Resources Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Agricultural Sciences
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureNational Eye InstituteNational Aeronautics and Space Administration
KeywordsEcohydrologyLoess plateauEnvironmental scienceVegetation (pathology)EvapotranspirationSurface runoffHydrology (agriculture)Plateau (mathematics)ChinaPrecipitationPhysical geographyLoessEcosystemSoil scienceGeologyEcologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

The Loess Plateau is a severely eroded and very venerable area in the northwestern China. Large scale vegetation restoration has been conducted in this region during the recent decades, its effect on the regional ecohydrology is under concern. In this study, long term satellite and derived data were used to analyze regional hydrological condition at the major part of the Loess Plateau (35°-37°N and 105°-110° E). The results indicate that there was an increase in the regional normalized difference vegetation index, evapotranspiration, rainfall intensity, soil water storage (surface 1m layer) and runoff. It was also observed that the total annual precipitation did not change significantly.The possible mechanisms may be related to the complicated processes of vegetation on ecohydrology. Our results and approach may be useful to evaluate the benefits of ecological restoration and further vegetation restoration at the Loess Plateau and other regions.

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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.027
GPT teacher head0.270
Teacher spread0.243 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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