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Record W1810646743 · doi:10.1093/jee/tov285

Insecticide Efficacy and Timing for Control of Western Bean Cutworm (Lepidoptera: Noctuidae) in Dry and Snap Beans

2015· article· en· W1810646743 on OpenAlexaff
Lindsey Goudis, Cheryl L. Trueman, Tracey Baute, Rebecca H. Hallett, Chris L. Gillard

Bibliographic record

VenueJournal of Economic Entomology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
Fundersnot available
KeywordsBiologyCutwormNoctuidaeLepidoptera genitaliaAgronomyPEST analysisPreharvestInfestationHorticultureBotany

Abstract

fetched live from OpenAlex

The western bean cutworm, Striacosta albicosta (Smith) (Lepidoptera: Noctuidae), is a recent pest of corn, dry,and snap beans, in the Great Lakes region, and best practices for its management in beans need to be established.Insecticide efficacy and application timing field studies, conducted in 2011–2013, determined that lambda-cyhalothrin and chlorantraniliprole were capable of reducing western bean cutworm feeding damage in dry beans from 2.3 to 0.4% in preharvest samples, and in snap beans from 4.8 to 0.1% of marketable pods, respectively. The best application timing in dry beans was determined to be 4–18 d after 50% egg hatch. No economic benefit was found when products were applied to dry beans, and despite high artificial inoculation rates, damage to marketable yield was relatively low. Thiamethoxam, methoxyfenozide, and spinetoram were also found to be effective at reducing western bean cutworm damage in dry bean to as low as 0.3% compared to an untreated control with 2.5% damaged pods. In snap beans, increased return on investment between CAD$400 and CAD$600 was seen with multiple applications of lambda-cyhalothrin, and with chlorantraniliprole applied 4 d after egg mass infestation.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.373

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 head0.022
GPT teacher head0.284
Teacher spread0.263 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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