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Record W2593570607 · doi:10.5539/sar.v6n2p58

The Response of Spring Wheat Cultivars to Arbuscular Mycorrhizal Colonization under Salinity Stresses

2017· article· en· W2593570607 on OpenAlexaffvenue
Daishu Yi, Timothy Schwinghamer, Yolande Dalpé, Jaswinder Singh, Shahrokh Khanizadeh

Bibliographic record

VenueSustainable Agriculture Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsAgriculture and Agri-Food CanadaMcGill University
Fundersnot available
KeywordsSalinityAgronomyBiologySymbiosisCultivarArable landArbuscular mycorrhizaAbiotic componentSoil salinityDry weightCropInoculationYield (engineering)HorticultureAgricultureBacteriaEcology

Abstract

fetched live from OpenAlex

Wheat is an important crop, playing inevitable roles in human life, ranging from major food resource to raw material for biofuel. However, due to the dramatically reduced available arable areas and increasingly severe abiotic and biotic stresses, wheat production nowadays faces extreme challenges.. Many approaches have been explored to increase wheat yield including development of new cultivars. One of the most promising approaches is the application of the naturally existent arbuscular mycorrhiza (AM), a mutualistic symbiosis originated over 400 million years ago. AM have long been known to form mutualistic symbiosis with various plants to enhance yield production and to improve stress tolerance, especially drought and salinity. But the benefits vary among AM strains and plant species. Therefore, the objective of the study was to investigate the influence of four AM strains colonized on four selected spring wheat varieties under three salt concentrations (0, 50, 100 mmol/L). The results demonstrated that wheat inoculated with arbuscular mycorrhizal strains Funneliformis mosseae and Rhizoglomusirregulare mitigated yield losses caused by increased salinity stresses as well as strengthened root growth in comparison with non-inoculated plant controls. Salinity stress, however, had non-significant negative effects on most variables, except for grain yield, root surface area and root dry weight, in which a significant decrease was observed in root surface area and root dry weight with the increasing of saline concentration.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.031
GPT teacher head0.319
Teacher spread0.288 · 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
Published2017
Admission routes2
Has abstractyes

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