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Record W2154944702 · doi:10.1603/0022-0493-93.3.559

Field Evaluation of Neem and Canola Oil for the Selective Control of the Honey Bee (Hymenoptera: Apidae) Mite Parasites <I>Varroa jacobsoni</I> (Acari: Varroidae) and <I>Acarapis woodi</I> (Acari: Tarsonemidae)

2000· article· en· W2154944702 on OpenAlexafffund
Adony P. Melathopoulos, Mark L. Winston, Robin Whittington, Heather Higo, Monique Le Doux

Bibliographic record

VenueJournal of Economic Entomology · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVarroa jacobsoniNeem oilBiologyCanolaApidaeAcaricideVarroaBotanyHoney beeMiteToxicologyHymenoptera

Abstract

fetched live from OpenAlex

Neem oil, neem extract (neem-aza), and canola oil were evaluated for the management of the honey bee mite parasites Varroa jacobsoni (Oudemans) and Acarapis woodi (Rennie) in field experiments. Spraying neem oil on bees was more effective at controlling V. jacobsoni than feeding oil in a sucrose-based matrix (patty), feeding neem-aza in syrup, or spraying canola oil. Neem oil sprays also protected susceptible bees from A. woodi infestation. Only neem oil provided V. jacobsoni control comparable to the known varroacide formic acid, but it was not as effective as the synthetic product Apistan (tau-fluvalinate). Neem oil was effective only when sprayed six times at 4-d intervals and not when applied three times at 8-d intervals. Neem oil spray treatments had no effect on adult honey bee populations, but treatments reduced the amount of sealed brood in colonies by 50% and caused queen loss at higher doses. Taken together, the results suggest that neem and canola oil show some promise for managing honey bee parasitic mites, but the negative effects of treatments to colonies and the lower efficacy against V. jacobsoni compared with synthetic acaricides may limit their usefulness to beekeepers.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.259
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 teacher head, not a consensus.

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

Citations42
Published2000
Admission routes2
Has abstractyes

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