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Record W2971406540 · doi:10.1093/jipm/pmz025

Ecology and Management of the Western Bean Cutworm (Lepidoptera: Noctuidae) in Corn and Dry Beans—Revision With Focus on the Great Lakes Region

2019· article· en· W2971406540 on OpenAlexaffabout
Jocelyn L Smith, Christina DiFonzo, Tracey Baute, Andrew P. Michel, Christian H. Krupke

Bibliographic record

VenueJournal of Integrated Pest Management · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
Fundersnot available
KeywordsCutwormPhaseolusNoctuidaePEST analysisBiologyAgronomyLepidoptera genitaliaRange (aeronautics)Integrated pest managementEcologyGeographyAgroforestryBotanyEngineering

Abstract

fetched live from OpenAlex

Abstract The western bean cutworm, Striacosta albicosta (Smith) is a native North American pest of corn and dry beans. The historical geographic range of the western bean cutworm covered the western Great Plains states including Colorado, Nebraska, and Wyoming. Since 1999, the geographic range of the western bean cutworm has rapidly expanded eastward across the U.S. Corn Belt and eastern Canada, causing significant and economic damage to corn Zea mays (L.) and dry edible beans Phaseolus spp., in parts of this region. Since 2010, increasing challenges related to managing this pest in its new range prompted numerous research studies that provided new insights into the biology and management of western bean cutworm. This revision of a previous Journal of IPM profile summarizes new information regarding the ecology and biology of western bean cutworm, and discusses updated recommendations for scouting and management in corn and dry beans, with an emphasis in the expanded geographic range of the Great Lakes region.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2019
Admission routes2
Has abstractyes

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