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Record W275139998 · doi:10.1201/9781482280340-4

He liothis/Helicoverpa Problem in the Americas: Biology and Management

2005· book-chapter· en· W275139998 on OpenAlexaboutno aff
David Bergvinson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
Fundersnot available
KeywordsHelicoverpaBiologyZoologyHelicoverpa armigeraEcology

Abstract

fetched live from OpenAlex

The com earworm, Helicoverpa zea (Boddie) and tobacco budworm, Heliothis virescens (Fabricius) occur throughout the temperate and tropical regions in the Americas. The com earworm, H. zea, is widely distributed in Canada (British Columbia, Manitoba, New Brunswick, Ontario, Quebec and Saskatchewan), Mexico and the USA (including Hawaii) in North America; and Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Falkland Islands, French Guiana, Guyana, Pern, Surinam, Umguay and Venezuela in South America. The tobacco budworm, H. virescens, is found throughout the eastern and southwestern United States, and also in California. It over-winters successfully only in the southern states. However, it occasionally survives in cold climates in greenhouses and other sheltered locations. Tobacco budworm disperses northward annually, and can be found in New England, New York, and southern Canada during late summer. It also occurs widely in the Caribbean, and sporadically in central and South America. In North America, H. zea is the second most important economic pest species (preceded only by codling moth) (Hardwick 1965). The estimated annual cost of damage by H. zea and H. virescens together on all crops in the USA is more than US$1000 million, despite an expenditure of US$ 250 million on insecticide application for controlling these pests (Fitt 1989).

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.275
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2005
Admission routes1
Has abstractyes

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