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Record W3006838313 · doi:10.4039/tce.2020.1

Role of fruit characters and colour on host selection of boreal fruits and berries by <i>Drosophila suzukii</i> (Diptera: Drosophilidae)

2020· article· en· W3006838313 on OpenAlexaff
Catherine M. Little, Peggy L. Dixon, Tom Chapman, N. Kirk Hillier

Bibliographic record

VenueThe Canadian Entomologist · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect behavior and control techniques
Canadian institutionsAcadia UniversityAgriculture and Agri-Food CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsDrosophila suzukiiBiologyDrosophila (subgenus)Host (biology)DrosophilidaeAttractionPEST analysisSweetnessBotanyInsectLarvaHorticultureZoologySugarEcologyFood scienceDrosophila melanogaster

Abstract

fetched live from OpenAlex

Abstract Continued range expansion of Drosophila suzukii (Matsumura) (Diptera: Drosophilidae) is exposing new species of soft fruits and berries to potential infestation. Our understanding of cues that drive host-finding and selection in this highly polyphagous pest insect is still incomplete. Fruit firmness influences host choice behaviour by limiting suitability for oviposition and larval development. Other factors such as fruit sweetness and acidity act as cues for fruit ripening. Here we assess the role of these cues and fruit colour on host selection. We demonstrate that the use of objective and nonanthropocentric methods of quantifying colour in studies of colour preference is critical to understanding the cues evoking responses from insects. Acidity but not sweetness increased D. suzukii attraction and larval success. Differences in D. suzukii attraction were most strongly correlated with short-wavelength reflectance (blue, cyan, and green (470–560 nm)). Growers could select for fruit varieties with relatively higher reflectance values upon maturity to reduce susceptibility to D. suzukii.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 communication0.0000.000
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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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 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

Citations16
Published2020
Admission routes1
Has abstractyes

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