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Females of <i>Cotesia vestalis</i> , a parasitoid of diamondback moth larvae, learn to recognise cues from aphid‐infested plants to exploit honeydew

2010· article· en· W2012403019 on OpenAlexaff
Soichi Kugimiya, Takeshi Shimoda, Jeremy N. McNeil, Junji Takabayashi

Bibliographic record

VenueEcological Entomology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsWestern University
Fundersnot available
KeywordsBiologyAphidHoneydewDiamondback mothMyzus persicaeBraconidaeParasitoidBrevicoryne brassicaeHost (biology)AphididaeParasitoid waspBotanyHomopteraPlutellaAgronomyBiological pest controlLarvaPEST analysisEcology

Abstract

fetched live from OpenAlex

1. To maximise their reproductive success, the females of most parasitoids must not only forage for hosts but must also find suitable food sources. These may be nectar and pollen from plants, heamolymph from hosts and/or honeydew from homopterous insects such as aphids. 2. Under laboratory conditions, females of Cotesia vestalis , a larval parasitoid of the diamondback moth ( Plutella xylostella ) which does not feed on host blood, survived significantly longer when held with cruciferous plants infested with non‐host green peach aphids ( Myzus persicae ) than when held with only uninfested plants. 3. Naïve parasitoids exhibited no preference between aphid‐infested and uninfested plants in a dual‐choice test, but those that had been previously fed aphid honeydew significantly preferred aphid‐infested plants to uninfested ones. 4. These results suggest that parasitoids that do not use aphids as hosts have the potential ability to learn cues from aphid‐infested plants when foraging for food. This flexible foraging behaviour could allow them to increase their lifetime reproductive success.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.000

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 head0.021
GPT teacher head0.246
Teacher spread0.226 · 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 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

Citations5
Published2010
Admission routes1
Has abstractyes

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