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Stimuli affecting selection of oviposition sites by female peach twig borer, <i>Anarsia lineatella</i> Zeller (Lepidoptera: Gelechiidae)

2008· article· en· W2021575960 on OpenAlexaff
Mark Sidney, Kendra Brown, Gary J. R. Judd, Gerhard Gries

Bibliographic record

VenueJournal of Applied Entomology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsAgriculture and Agri-Food CanadaSimon Fraser University
Fundersnot available
KeywordsBiologyGelechiidaeTwigLepidoptera genitaliaHorticultureBotanyInstarPupaLarvaOvipositorHymenoptera

Abstract

fetched live from OpenAlex

Abstract In laboratory and field experiments, stimuli were tested that might affect oviposition decisions by female peach twig borer moths, Anarsia lineatella Zeller (Lepidoptera: Gelechiidae). When given a choice between immature green peach fruits, green mature peach fruits and soft‐ripe peach fruits, the latter received the fewest eggs. Fuzzy halves of peach fruits received ten times more eggs then shaved hairless halves. Volatiles from both almond and peach shoots induced more oviposition by females than by control stimuli. Similarly, volatiles from immature green peach fruits, mature green or mature hard‐ripe peach fruits induced more oviposition than their respective control stimuli. In a choice experiment, volatiles from immature peach fruit stimulated three times more oviposition than those from soft‐ripe peach fruit. Discrimination against mature soft‐ripe peach fruits as potential oviposition sites may lie in the phenology of A. lineatella and host peach fruits. Larval development to the pupal stage takes 15–27 days. Therefore, any eggs laid on a ripe fruit 14 days before it falls from the tree will not likely develop into adult insects because developing larvae will only reach third or fourth instar before the fruit is decomposed, and only first and second instar larvae can overwinter.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.613

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 head0.225
Teacher spread0.211 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

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