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Record W2783944602 · doi:10.5539/jas.v10n2p148

Effectiveness of Silver Nanoparticles against Root-Knot Nematode, Meloidogyne incognita Infecting Tomato under Greenhouse Conditions

2018· article· en· W2783944602 on OpenAlexvenueno aff
A. H. Nour El-Deen, Bahig Ahmed El-Deeb

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsMeloidogyne incognitaRoot-knot nematodeSilver nitrateNematodeTerra incognitaBiologyHorticultureGreenhouseSilver nanoparticleRhizomeNematode infectionAgronomyChemistryBotanyNanoparticleMaterials scienceNuclear chemistry

Abstract

fetched live from OpenAlex

Root-knot nematode, Meloidogyne incognita is the most economically important plant parasitic nematode species that cause serious damage to most agricultural crops including tomato worldwide. Nematicides do not provide long-term suppression of root-knot nematodes, and environmental and human health concerns are resulting in increased restrictions on their use. A greenhouse experiment was conducted to evaluate the effect of silver nanoparticles (AgNPs) at concentrations of 0.25, 0.5 and 1 mM as a potential nematicide on M. incognita infecting tomato. AgNP was biologically and chemically synthesized by a reaction of silver nitrate with ginger (Zingiber officinale) rhizomes aqueous extract and sodium borohydride, respectively. Results indicated that application of AgNPs improved plant growth and reduced nematode infection in comparison to silver nitrate and control treatments. The highest increment of fresh weight as well as the lowest numbers of galls and egg-masses was obtained when tomato plants was treated with AgNP produced by ginger extract at 1 mM.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.015
GPT teacher head0.250
Teacher spread0.235 · 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 designBench or experimental
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

Citations32
Published2018
Admission routes1
Has abstractyes

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