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Record W2070736789 · doi:10.1103/physreve.76.017301

Micronanostructures of the scales on a mosquito’s legs and their role in weight support

2007· article· en· W2070736789 on OpenAlexaff
Chengwei Wu, Xiangqing Kong, Diane Wu

Bibliographic record

VenuePhysical Review E · 2007
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMicroscale chemistryNanoscopic scaleWater bodyNanostructureMaterials scienceSurface (topology)MechanicsNanotechnologyGeometryGeologyPhysicsMathematicsGeotechnical engineering

Abstract

fetched live from OpenAlex

We show here that the mosquito cannot only give rise to a higher water-supporting force than the water strider if the ratio of the water-supporting force to the body weight of the insect itself is compared, but also can safely take off or land on the water surface, and also can attach on any solid surface like the fly. We found that the mosquito's legs are covered by numerous scales consisting of the uniform microscale longitudinal ridges (nanoscale thickness and microscale spacing between) and nanoscale cross ribs (nanoscale thickness and spacing between). Such special delicate microstructure and/or nanostructure on the leg surface give a water contact angle of approximately 153 degrees and give a surprising high water-supporting ability. It was found that the water-supporting force of a single leg of the mosquito is about 23 times the body weight of the mosquito, compared with a water strider's leg giving a water-supporting force of about 15 times the body weight of the insect.

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 score0.001
Threshold uncertainty score0.003

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 head0.006
GPT teacher head0.246
Teacher spread0.240 · 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

Citations70
Published2007
Admission routes1
Has abstractyes

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Same venuePhysical Review ESame topicAdhesion, Friction, and Surface InteractionsFrench-language works237,207