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Record W2578582005 · doi:10.5539/jas.v9n2p1

Mycorrhizal Inoculation Increases Growth and Induces Changes in Specific Polyphenol Levels in Olive Saplings

2017· article· en· W2578582005 on OpenAlexvenueno aff
Nasir S. A. Malik, Alberto Núñez, Lindsay C. McKeever

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPolyphenolOleuropeinInoculationOleaHorticultureVerbascosideBiologyFungusBotanyChemistryFood scienceGlycosideAntioxidant

Abstract

fetched live from OpenAlex

This study was conducted to investigate the effect of mycorrhizal symbiosis on the levels of polyphenols in olive saplings. Rooted stem cuttings of olive cultivar, ‘Arbequina’, were inoculated with AM fungus Rhizophagus intraradices. The inoculated plants showed more robust growth after six months, and after nine months the increase in the mycorrhizal plant’s height was 146%, and the increase in number of leaves was 117% when compared to uninoculated controls. Polyphenols in the methanol extracts of leaves were separated by HPLC and the peaks identified by using commercially available standard compounds and comparing retention time and the mass obtained with the mass spectrometer. Oleuropein, which is a major component of the olive leaf polyphenols, increased in mycorrhizal plants compared to uninoculated plants by 42%, and its derivatives, oleuroside and ligstroside, increased by 68% and 48%, respectively. The highest increase was found in the levels of luteolin-7’-O-glucoside (107% increase), while its sister compound luteolin-4’-O-glucoside increased by 43%. Only verbascoside levels were lower in mycorrhizal plants versus non-mycorrhizal plants declining to below detectable limits. Thus, inoculation of olive saplings with mycorrhizal fungi produces very positive effects on the levels of olive leaf polyphenols. Higher levels polyphenols mean better quality of leaf material for use as herbal medicine.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.276
Teacher spread0.241 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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