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Record W2553587624 · doi:10.1038/srep37026

Avoidance, escape and microstructural adaptations of the tea green leafhopper to water droplets

2016· article· en· W2553587624 on OpenAlexaff
Meizhen Lin, Liette Vasseur, Guang Yang, Geoff M. Gurr, Minsheng You

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsBrock University
FundersMinistry of Agriculture of the People's Republic of ChinaFujian Agriculture and Forestry UniversityMinistry of Science and Technology of the People's Republic of China
KeywordsLeafhopperNymphInsectBiologyWettingDewBotanyAgronomyMaterials scienceComposite materialHemipteraMeteorologyCondensation

Abstract

fetched live from OpenAlex

Rain and dew droplets can dislodge or drown small insects and may be important factors that drive adaptations for avoidance and escape. Studying the microstructure of small insects and insect behaviour can help understand these adaptations. We quantified avoidance behaviour and entrapment of nymphs and adults of the tea green leafhopper (Empoasca onukii) using simulated rainfall onto host plant foliage and made observations of pretarsi and abdomen microstructures. Adults responded rapidly to simulated rainfall and escaped by jumping whilst most young nymphs were washed from water-sprayed leaves though older nymphs tended to remain on leaves and subsequently escaped from water droplets. Adults had denser covering of water-repelling brochosomes on pretarsi and abdomen surface than nymphs, and were able to stand on water film whilst most nymphs had multiple penetrating tarsi. Removal of brochosomes from the abdomen of adults reduced hydrophobicity, demonstrating the hydrophobic significance of brochosomes in the capacity of leafhopper to escape from water droplets. Nymphs exhibited a higher pull-off force than adults. This research is one of the few studies to focus on the wettability and water avoidance of small insect pests and has implications for pest management.

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)0.001
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 score0.030
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.221
Teacher spread0.208 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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