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Record W3000626173 · doi:10.1080/11956860.2019.1710908

Impacts of livestock grazing on vegetation characteristics and soil chemical properties of alpine meadows in the eastern Qinghai-Tibetan Plateau

2020· article· en· W3000626173 on OpenAlexvenueno aff
Lei Ji, Yan Qin, Saheed Olaide Jimoh, Xiangyang Hou, Na Zhang, Yuanjia Luo

Bibliographic record

VenueEcoscience · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsGrazingVegetation (pathology)Environmental sciencePlateau (mathematics)Biomass (ecology)Soil carbonAgronomyOrganic matterCanopyEcologySoil waterSoil scienceBiology

Abstract

fetched live from OpenAlex

Livestock grazing is one of the significant causes of land degradation. However, the effect of contrasting grazing intensities on soil properties and vegetation in the southeastern Qinghai-Tibetan Plateau (QTP) is poorly understood. We studied the impact of light grazing (LG), moderate grazing (MG), heavy grazing (HG) and no grazing (NG) on vegetation characteristics and the chemical properties of soil samples taken at 0–10 cm, 10–20 cm and 20–30 cm layers from the designated grazing treatments. A total of 42 species representing 32 genera and 16 families were identified. Our result shows that HG significantly reduced total aboveground biomass, vegetation cover, canopy average height, but increased unpalatable aboveground biomass. Soil organic matter declined with increasing grazing intensity and respectively decreased to 64.51%, 65.38% and 82.40% for LG, MG and HG compared to the NG treatment and soil carbon storage exhibited a similar pattern. Soil total nitrogen and phosphorus contents decreased with increasing soil depth, while soil total potassium was not affected by grazing across soil depths. We conclude that 1 yak would have a more severe impact than 3 sheep units on the vegetation community and soil characteristics of alpine meadows in the southeastern QTP.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.189

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)0.0000.000

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 head0.020
GPT teacher head0.208
Teacher spread0.187 · 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

Citations28
Published2020
Admission routes1
Has abstractyes

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