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Record W2138035971 · doi:10.1603/ec10331

Flight Mill Performance of the Lacewing Chrysoperla sinica (Neuroptera: Chrysopidae) as a Function of Age, Temperature, and Relative Humidity

2011· article· en· W2138035971 on OpenAlexaff
Zhongfang Liu, Jeremy N. McNeil, Kongming Wu

Bibliographic record

VenueJournal of Economic Entomology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsWestern University
FundersHenan Academy of Agricultural SciencesMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsNeuropteraChrysopidaeBiologyRelative humidityContext (archaeology)PredatorChrysoperla carneaZoologyToxicologyEcologyLarvaPredationPhysicsMeteorology

Abstract

fetched live from OpenAlex

The lacewing Chrysoperla sinica (Tjeder) (Neuroptera: Chrysopidae) is an important predator of several insect pests in China and has considerable potential as a biological control agent. An inoculative approach would be the releasing adults early in the season to ensure that populations are present before pest densities increase. However, an understanding of adult flight activity under different conditions is necessary to develop appropriate release strategies. Therefore, we used a 32-channel, computer-monitored flight mill system to determine the effect of age on the flight activity of unmated female and male adults. Both sexes had high total flight activity levels as well as the longest individual flight bouts 2 and 3 d after emergence. The effects of temperature (between 13 and 33 degrees C at 75% RH) and relative humidity (between 30 and 90% RH at 23 degrees C) on the flight activity of 3-d-old unmated adults also were determined. Flight activity declined at the lowest (13 degrees C) and highest (33 degrees C) temperatures tested, as well as at the lowest relative humidity (30% RH). These findings are discussed within the context of selecting the appropriate environmental conditions for releasing C. sinica.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.022
GPT teacher head0.216
Teacher spread0.194 · 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 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

Citations30
Published2011
Admission routes1
Has abstractyes

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