MétaCan
Menu
Back to cohort
Record W4403767172 · doi:10.1111/jen.13364

Short‐Term Grasslands in Agriculture Support Both Natural Enemy and Phytophagous Arthropod Populations

2024· article· en· W4403767172 on OpenAlexafffund
Matthew Tsuruda, Martina Clausen, Drew Bondar, Claire Kremen, Juli Carrillo

Bibliographic record

VenueJournal of Applied Entomology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsDelta-Q Technologies (Canada)University of British Columbia
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaMitacsDalhousie University
KeywordsBiologyArthropodEcologyTerm (time)Natural enemiesNatural (archaeology)AgricultureAgroforestry

Abstract

fetched live from OpenAlex

ABSTRACT Simplified agricultural land with high chemical input is increasingly replacing natural habitats in many parts of the world. This loss and fragmentation of natural areas may have profound impacts on an array of wildlife, including predatory arthropods that provide natural biocontrol of agricultural pests. There is increasing interest in utilising short‐term seminatural habitat restoration in agricultural systems to support populations of beneficial organisms. We assessed the impact of two types of short‐term set‐asides on the diversity and abundance/activity density of phytophagous and predatory arthropods. We found that flower‐enhanced set‐asides supported higher abundances of both phytophagous and predatory arthropods compared to control crop fields. In addition, we found that both set‐aside types (flower enhanced and traditional) supported a more active and diverse community of beneficial predatory carabid beetles, with particularly strong trends in set‐asides supplemented with flowering plants. These results suggest that small‐scale, temporary, seminatural habitats in agricultural systems provide support for a diverse and abundant community of both beneficial and phytophagous arthropods.

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 score0.934
Threshold uncertainty score0.161

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied EntomologySame topicPlant Parasitism and ResistanceFrench-language works237,207