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Record W2417552253 · doi:10.1093/ee/nvw010

Exposure to Guava Affects Citrus Olfactory Cues and Attractiveness to<i>Diaphorina citri</i>(Hemiptera: Psyllidae)

2016· article· en· W2417552253 on OpenAlexaff
Jagadish Chandra Barman, S. Campbell, Xinnian Zeng

Bibliographic record

VenueEnvironmental Entomology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytoplasmas and Hemiptera pathogens
Canadian institutionsUniversity of Toronto
FundersBhabha Atomic Research CentreSouth China Agricultural University
KeywordsDiaphorina citriBiologyHemipteraIntercroppingPEST analysisPsidiumAttractivenessHorticultureBotanyAgronomy

Abstract

fetched live from OpenAlex

Intercropping can reduce agricultural pest incidence, and represents an important sustainable alternative to conventional pest control methods. Understanding the ecological mechanisms for intercropping could help optimize its use, particularly in tropical systems which present a large number of intercropping possibilities. Citrus is threatened worldwide by greening disease (huanglongbing, HLB) vectored by the Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Psyllidae). Control of HLB and citrus psyllid can be partially achieved through intercropping with guava, Psidium guajava L., but the mechanisms remain unclear. We tested the hypothesis that guava olfactory cues affect psyllid behavior by altering the attractiveness of citrus through plant-plant interactions. In choice and no-choice cage experiments, psyllid settlement was reduced on citrus shoots that had been exposed to guava shoot odors for at least 2 h. In Y-tube olfactometer experiments, psyllids oriented to odors of unexposed, compared with guava-exposed, citrus shoots. These behavioral results indicate that a mechanism for the success of guava intercropping for sustainable, ecological disease management may be the indirect effect of guava on citrus attractiveness.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

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)0.0010.000

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 head0.014
GPT teacher head0.203
Teacher spread0.188 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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