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Record W2465751348 · doi:10.1111/afe.12170

Weed seed granivory by carabid beetles and crickets for biological control of weeds in commercial lowbush blueberry fields

2016· article· en· W2465751348 on OpenAlexafffund
G. Christopher Cutler, Tess Astatkie, G. S. Chahil

Bibliographic record

VenueAgricultural and Forest Entomology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsDalhousie University
FundersAgri-Futures Nova Scotia AssociationWild Blueberry Producers Association of Nova Scotia
KeywordsBiologyWeedBiological pest controlAgronomyHorticulture

Abstract

fetched live from OpenAlex

Abstract Weeds are one of the most limiting factors in the commercial production of lowbush blueberries V accinium angustifolium ( E ricaceae). Sheep sorrel ( R umex acetosella ) and hairy fescue ( F estuca tenuifolia ) are prominent weeds in lowbush blueberry fields. Because the granivorous insects H arpalus rufipes ( C arabidae) and G ryllus pennsylvanicus ( G ryllidae) are common in lowbush blueberry fields when sheep sorrel and hairy fescue are dispersing seeds, we examined how granivorous insects can contribute to the biocontrol of these weeds. In the laboratory, H . rufipes and G . pennsylvanicus consumed a significant number of seeds of sheep sorrel and hairy fescue, and a field experiment found that insects probably consume a significant number of sheep sorrel and hairy fescue seeds in blueberry fields. Additional experiments found that H . rufipes was highly susceptible to field rates of phosmet and acetamiprid, although not to field rates of spirotetramat, which are insecticides that may be used in blueberry fields when the beetle is active. Natural populations of granivorous insects probably provide a valuable ecological service in commercial lowbush blueberry fields and should be conserved in the development of integrated weed management programmes.

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 score0.845
Threshold uncertainty score0.421

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 head0.010
GPT teacher head0.204
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

Citations14
Published2016
Admission routes2
Has abstractyes

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