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Record W2792248931 · doi:10.1134/s199542551801002x

Controlling Soil Factor in Plant Growth and Salt Tolerance of Leguminous Plant Alhagi sparsifolia Shap. in Saline Deserts, Northwest China

2018· article· en· W2792248931 on OpenAlexaff
Bo Zhang, Dongwei GUI, Xiaopeng Gao, Muhammad Shareef, Lei Li, Fanjiang Zeng

Bibliographic record

VenueContemporary Problems of Ecology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsUniversity of Manitoba
FundersChinese Academy of Sciences
KeywordsSoil salinityAgronomyEcosystemSoil waterSoil pHEnvironmental scienceSalinityChemistryEcologyBiologySoil science

Abstract

fetched live from OpenAlex

The understanding of relationships between the plants and environmental variables is important for ecological restoration. The objective of this research was to investigate the controlling soil factor in plant growth and salt tolerance of leguminous plant Alhagi sparsifolia Shap. ( A. sparsifolia ) in a saline desert ecosystem of western China. Results showed that coverage of A. sparsifolia was positively linked to soil total K. A. sparsifolia accumulated the toxic Na + in either roots or stems and thus reduced their allocation in the leaves to adapt the salt stress. The most important factor that determined Na + uptake of A. sparsifolia was soil total K content, which suggests that total K can alleviate toxic Na + accumulation in A. sparsifolia . In addition, there was a significant negative correlation among toxic Cl − concentration in A. sparsifolia , soil total K and pH. Overall, soil total K accounted for 18.2% of plant growth and salt tolerance of A. sparsifolia , followed by soil pH 13.1%, and soil total P 11.6%. In order to restore A. sparsifolia in the degraded desert ecosystems on salinized soil, first we need to consider the effects of soil total K, and then synergistically consider the soil pH and total P.

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 score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.030
GPT teacher head0.213
Teacher spread0.183 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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