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Electronic tags for the tracking of insects in flight: effect of weight on flight performance of adult Colorado potato beetles

2001· article· en· W2057970593 on OpenAlexafffund
Gilles Boiteau, Bruce G. Colpitts

Bibliographic record

VenueEntomologia Experimentalis et Applicata · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect behavior and control techniques
Canadian institutionsUniversity of New BrunswickAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsLeptinotarsaColorado potato beetleBiologyWingBiological dispersalPEST analysisRange (aeronautics)HorticultureWing loadingBotanyPopulationPhysics

Abstract

fetched live from OpenAlex

Abstract The wing loading of the Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae), was found to decrease slightly with increasing size over a wide range of individual sizes and independent of sex. This makes it possible to use tags of the same weight for beetles of all sizes and suggests that if the addition of light electronic tags has any effect on the beetle's flight it will be similar across beetle size. The wing loading of individual potato beetles ranged from an average minimum 10.9 N m−2 to an average maximum of 15.6 N m−2 as their weights fluctuated over time following water and food uptake or dispersal. However, tests carried out in flight chambers indicate that beetles become incapable of upward flight as they go beyond an average wing loading of 11.8 N m−2, that is 101 μN (10.3 mg) beyond their minimum weight. It is estimated from our results that electronic tags should weigh no more than 23–33% of the potato beetle's acceptable extra loading for the technique to have no or minimal impact on the number and quality of upward flights taken.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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 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

Citations46
Published2001
Admission routes2
Has abstractyes

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