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Record W4403471410 · doi:10.1007/s11829-024-10103-3

Trap cropping for insect pests in the Canadian Prairies: a review and a case study

2024· review· en· W4403471410 on OpenAlexafffundabout
Héctor A. Cárcamo, James A. Tansey, Brian L. Beres, Haley A. Catton, Breanne D. Tidemann, Peter Reid, Meghan A. Vankosky

Bibliographic record

VenueArthropod-Plant Interactions · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsSaskatchewan Ministry of AgricultureAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaAlberta Pulse Growers CommissionSaskatchewan Pulse GrowersAlberta Crop Industry Development Fund
KeywordsTrap (plumbing)CroppingInsectTrap cropGeographyAgroforestryBiologyAgronomyEcologyIntegrated pest managementAgricultureMeteorology

Abstract

fetched live from OpenAlex

Abstract The Canadian Prairies are one of the major agricultural regions of the world in terms of cereal, oilseed and pulse crop production. With few exceptions, major insect pests like grasshoppers, flea beetles, Lygus bugs, wireworms and pea leaf weevils are controlled with insecticides. Wheat stem sawfly is managed through host plant resistance and endemic natural enemies, whereas cereal leaf beetle is managed through classical biological control. Large farms and short growing seasons in the region present logistical challenges to adopt time intensive pest management systems such as trap crops. Therefore, there is no adoption of trap crops even though some research has demonstrated their potential. In this article we present a brief overview of the pest status and management, and we summarize research on trap crops in the Prairies Ecozone and adjacent ecoregions. We conclude the review with some innovative research ideas to make trap cropping a more appealing pest management system in our quest to reduce dependency on chemical insecticides and increase the environmental resilience of Canadian agroecosystems.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.096
GPT teacher head0.354
Teacher spread0.258 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2024
Admission routes3
Has abstractyes

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