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Record W2371729920

Ecological benefit of land use/cover change in endorheic drainage——A case study on the middle and lower reacher of Shule River

2013· article· en· W2371729920 on OpenAlexaff
MA Zhong-hua

Bibliographic record

VenueGanhanqu ziyuan yu huanjing · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsScience North
Fundersnot available
KeywordsArable landEcosystem servicesLand useGrasslandEnvironmental scienceLand use, land-use change and forestryLand coverEcosystemLand developmentEnvironmental resource managementEcologyGeographyWater resource managementEnvironmental protectionHydrology (agriculture)Agriculture
DOInot available

Abstract

fetched live from OpenAlex

Based on land use data of the middle and lower reaches of Shule River in Western China in 1985,2000 and 2010,we analyzed the eco-efficiency of land use/cover change in study area from the view of the ecosystem service function according to Costanza's method and equivalent factor table of ecological service value modified by Gao-di Xie et al.The results were as follows: with land use intensity increasing rapidly,the total value of ecosystem services was slow growing,only 0.02 million yuan from 1985 to 2010,and the ecological effect of excessive use of land had been highlighted in some area because of land ecological overload.Grass played an important role in the ecosystem of study area.The changes of arable land,grassland and forest had a greater impact on the value of ecosystem services in the study area.Coefficient of sensitivity analysis showed that the ecological service value was not sensitive to the change of ecological service value coefficient in study area.So the result was credible.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
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)0.0020.000

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 head0.058
GPT teacher head0.236
Teacher spread0.178 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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