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Record W7134931495 · doi:10.5376/me.2024.15.0029

Research on Insect Resistance Mechanisms in Loquat and Their Application in Pest Management

2024· article· W7134931495 on OpenAlexvenueno aff
Yiwei Li, Xianquan Qin, Liyu Liang, Jin Wang, Xi Wang, Hongli Li

Bibliographic record

VenueMolecular Entomology · 2024
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated pest managementResistance (ecology)PEST analysisInsect pestInsectPest control

Abstract

fetched live from OpenAlex

Loquat ( Eriobotrya japonica ) is a valuable fruit crop with increasing global cultivation, but it faces significant challenges from pest infestations, which threaten yield and fruit quality. Enhancing insect resistance in loquat is crucial for sustainable crop protection. This study explores the mechanisms of insect resistance in loquat, focusing on plant structural defenses, phytochemical production, and genetic resistance mechanisms. Advances in molecular research, including the identification of resistance genes and breeding strategies, are also discussed. Integrated pest management (IPM) approaches that incorporate insect-resistant varieties are examined, with a case study demonstrating the effectiveness of these strategies in real-world loquat production. This study highlights the importance of developing insect-resistant loquat varieties as a sustainable solution to pest management challenges and outlines future directions for genetic enhancement and global application.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.302
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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