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Record W2905603742 · doi:10.1017/s0031182018002056

Prevalence of a nematode castrator of the carrot weevil and impact on fecundity and survival

2018· article· en· W2905603742 on OpenAlexafffund
Annie‐Ève Gagnon, Guy Boivin, Guy Bélair, Benjamin Mimee

Bibliographic record

VenueParasitology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsBiologyFecundityWeevilNematodeHost (biology)PEST analysisLarvaParasite hostingZoologyVeterinary medicineBotanyEcologyPopulation

Abstract

fetched live from OpenAlex

Bradynema listronoti is a parasitic nematode described from infected specimens of the carrot weevil Listronotus oregonensis. Prevalence of infection by B. listronoti under field conditions was followed over a period of 16 years in an untreated carrot field. Susceptibility of different carrot weevil life stages was evaluated as well as the impact of infection on fecundity and mortality. Gene expression in infected and uninfected carrot weevils was also compared to evaluate the impact of the parasite on the host transcriptome. Prevalence of B. listronoti in carrot weevil populations was sustained over the years ranging from 20 to 63%. All the weevil stages exposed to B. listronoti inoculum were susceptible to infection, larvae being more vulnerable (59 ± 8% infected) compared with pupae (4 ± 3% infected) and adults (7 ± 3% infected). The fecundity of infected female weevils was greatly reduced (60-fold) due to an inhibition of the maturation of the reproductive system. Transcriptomic analyses revealed that this parasitic castration may have been triggered by the inhibition of reproductive hormone production. The B. listronoti-L. oregonensis interaction represents a case of parasitic castration with a unique potential for biological control of an important pest of carrots.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.097

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 head0.021
GPT teacher head0.292
Teacher spread0.271 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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