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Record W2162899046 · doi:10.1002/2015jd023826

Nonstationary desertification dynamics of desert oasis under climate change and human interference

2015· article· en· W2162899046 on OpenAlexafffund
Guohe Huang, Xiaosheng Qin, Li He, Hua Zhang, Yongping Li, Zhong Li

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsUniversity of Regina
FundersU.S. Geological SurveyHigher Education Discipline Innovation ProjectNatural Sciences and Engineering Research Council of CanadaChina Meteorological AdministrationChinese Academy of Sciences
KeywordsDesertificationAridDesert (philosophy)Climate changeEnvironmental scienceField (mathematics)Global changeEcologyClimatologyGeographyEnvironmental resource managementGeologyBiologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Desertification is becoming a major ecological concern in arid and semiarid regions, especially under climate change. Globally, it is burning up lands for human habitats with a rapidly spreading tendency. Many scientists have been struggling to explore the related mechanisms. Challenges remain in revealing the fundamental principle in terms of desert‐oasis interactions that are associated with nonstationary variations. Here we present a theory of desertification dynamics through examining nonstationary effects of climate change and human interference. We hypothesize that such dynamics can be described as the fate and transport of dry air mass continuously generated from desert. We simulate a region in northwestern China and reveal that dynamics of the nonstationary desertification process is subject to interactive impacts from a variety of factors. Our study moves forward the field of desertification studies through initiation of the dynamics and nonstationarity concepts which allow the fundamental mechanism to be disclosed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.078
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.084
GPT teacher head0.346
Teacher spread0.262 · 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.

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

Citations10
Published2015
Admission routes2
Has abstractyes

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