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Record W3042978499 · doi:10.1080/07060661.2020.1778789

Induction of resistance to<i>Meloidogyne graminicola</i>in rice

2020· article· en· W3042978499 on OpenAlexvenueno aff
Mayra Renata Cruz Soares, Cláudia Regina Dias‐Arieira

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsResistance (ecology)GraminicolaAgronomyBiologyBotanyHorticultureGeneticsGene

Abstract

fetched live from OpenAlex

Root-knot nematodes are responsible for substantial losses in rice production. These parasites are difficult to control, particularly in flooded fields, which require carefully designed strategies to avoid contamination of water bodies. This study investigated whether biotic and abiotic elicitors can induce resistance to the root-knot nematode Meloidogyne graminicola in rice plants. An initial screening test was performed to determine the elicitors with the greatest potential to control nematodes. Then, a second experiment was conducted under net house conditions to assess the effects of a mannanoligosaccharide-based fertilizer (MOS), acibenzolar-S-methyl (ASM), and silicate clay on nematode reproduction, penetration, and activation of plant defence-related enzymes. Elicitor treatment reduced nematode reproduction by 57.4 to 75.6% at 60 days after inoculation (DAI). Two factors that may have contributed to this result include reduced nematode penetration and delayed development, as observed in the screening test. All elicitors increased phenylalanine ammonia-lyase activity at 10 DAI. MOS and silicate clay also increased catalase, peroxidase, and glucanase activity at 8 DAI. Elicitor treatment activates defence responses in rice plants and may be an environmentally friendly strategy for controlling M. graminicola in an integrated management system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.995

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 head0.029
GPT teacher head0.197
Teacher spread0.168 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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