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Age modifies the effect of body size on fecundity in Culex quinquefasciatus Say (Diptera: Culicidae)

2009· article· en· W2039846343 on OpenAlexaff
Sean McCann, Jonathan F. Day, Sandra A. Allan, Cynthia C. Lord

Bibliographic record

VenueJournal of Vector Ecology · 2009
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsSimon Fraser University
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of Health
KeywordsFecundityBiologyCulex quinquefasciatusBlood mealContext (archaeology)ZoologyPopulationMealAnimal scienceEcologyLarvaDemographyFood science

Abstract

fetched live from OpenAlex

Fecundity of mosquitoes can vary with many factors and can have a strong effect on population growth. This study reports the effects of body size, blood meal size, and age on the reproductive output of nulliparous Culex quinquefasciatus, a vector of arboviruses and other pathogens. Mated adult female mosquitoes from a colony were reared under standard conditions and fed on chickens at different ages post-eclosion. Blood meal size and wing length were recorded, as well as the number of eggs in the first-cycle egg raft. Each of these factors had a significant influence on fecundity considered in a simple regression context. Multiple regression analysis revealed a significant interaction effect between age and body size on fecundity. Up to 13 days of age, fecundity was positively correlated with body size, but in mosquitoes older than 13 days, this relationship was not significantly different from zero. These results are discussed in terms of the known physiology of this and other species.

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 score0.004

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 head0.276
Teacher spread0.270 · 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

Citations61
Published2009
Admission routes1
Has abstractyes

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