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Cocoon‐spinning larvae of Oriental fruit moth and Indianmeal moth do not produce aggregation pheromone

2009· article· en· W2051677881 on OpenAlexafffund
Zaid Jumean, Leila Fazel, Charlene Wood, Thomas Cowan, Maya L. Evenden, Gerhard Gries

Bibliographic record

VenueAgricultural and Forest Entomology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pheromone Research and Control
Canadian institutionsSimon Fraser UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTortricidaeLepidoptera genitaliaCodling mothBiologyPyralidaePheromoneLarvaSex pheromoneBotanyInstarPupaHorticulture

Abstract

fetched live from OpenAlex

Abstract 1 Mature larvae of the Oriental fruit moth (OFM) Grapholita molesta (Lepidoptera: Tortricidae) and the Indianmeal moth (IMM) Plodia interpunctella (Lepidoptera: Pyralidae) leave their food source in search of suitable pupation sites in which to spin cocoons. These sites are typically well‐concealed cracks and crevices within the environment. Such cocooning behaviour is also observed in larvae of the codling moth (CM) Cydia pomonella (Lepidoptera: Tortricidae), which aggregate prior to pupation in response to a pheromone blend produced by cocoon‐spinning conspecific larvae. 2 In laboratory experiments, we tested whether cocoon‐spinning OFM and IMM larvae produce aggregation pheromones and whether CM larvae are cross‐attracted to closely‐related OFM larvae. 3 Fifth‐instar OFM and IMM larvae were not attracted to, or arrested by, cocoon‐spinning conspecifics. Moreover, fifth‐instar CM larvae were not cross‐attracted to either cocoon‐spinning OFM or IMM larvae. 4 Analyses of volatiles released from cocoon‐spinning OFM and IMM larvae revealed that both OFM and IMM lack components that are present in the aggregation pheromone of CM larvae. This information may help explain why CM larvae are not cross‐attracted to cocooning OFM or IMM larvae.

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 score0.927
Threshold uncertainty score0.274

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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