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Impact of Trap Architecture, Adjacent Habitats, Abiotic Factors, and Host Plant Phenology on Captures of Plum Curculio (Coleoptera: Curculionidae) Adults

2007· article· en· W2174613057 on OpenAlexaff
Gérald Lafleur, Gérald Chouinard, Charles Vincent, Daniel Cormier

Bibliographic record

VenueJournal of Economic Entomology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect behavior and control techniques
Canadian institutionsInstitut de Recherche et de Développement en Agroenvironnement
Fundersnot available
KeywordsCurculioBiologyOrchardCurculionidaePhenologyMalusHorticultureHost (biology)PEST analysisAbiotic componentBotanyAgronomyEcology

Abstract

fetched live from OpenAlex

Pyramid traps, 2.44 m and 3.66 m in height, were compared with standard-sized pyramid traps, 1.22 m in height, to assess the impact of trap architecture on captures of adult plum curculio, Conotrachelus nenuphar (Herbst) (Coleoptera: Curculionidae), in two apple (Malus spp.) orchards and a blueberry (Vaccinium spp.) planting. The effects of adjacent habitat (organic orchard versus wooded areas), abiotic factors, and phenological stages of apple also were assessed to determine whether these variables influenced trap captures. Standard-sized pyramidal traps captured significantly more adults than larger trap variants. In the apple orchards, most adults (70-80%) were captured before petal fall with the exception of blocks adjacent to the organic orchard (25%). Significantly more adults were captured along the edge of an apple orchard (managed using an integrated pest management strategy) facing an organic apple orchard (76%) than along the edge facing wooded areas (24%). There was a significant positive correlation between daily trap captures and mean daily temperatures before petal fall in apple orchards.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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

Citations6
Published2007
Admission routes1
Has abstractyes

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