Semiochemical attractants for the beech leaf‐mining weevil,<i><scp>O</scp>rchestes fagi</i>
Bibliographic record
Abstract
Abstract The beech leaf‐mining weevil,Orchestes fagiL. (Curculionidae:Curculioninae:Rhamphini), a pest of European beech,Fagus sylvaticaL. (Fagaceae), was recently discovered infesting American beech,Fagus grandifoliaEhrh., in Nova Scotia, Canada. AdultO. fagifeed on both young and mature leaves of beech as well as on other species (e.g., raspberry,Rubusspp.), but oviposition and larval feeding are restricted to beech. Females oviposit in young developing beech leaves at the time of bud burst. We characterized volatiles emitted from buds, leaves, and sapwood of American beech and examined their potential as attractants alone or when combined with other weevil pheromones forO. fagi. We predicted that adults would be attracted to volatiles emitted from beech leaves, especially those emitted from bursting beech buds. Gas chromatography/mass spectrometry (GC/MS) analyses of volatiles collected from buds at pre‐ and post‐budburst identified two diterpene hydrocarbons, 9‐geranyl‐p‐cymene (1) and 9‐geranyl‐α‐terpinene (2a), that were emitted in large amounts at the time of bud burst. Compound1significantly increased mean catch of males and totalO. fagi(but not females) on sticky traps compared with unbaited controls. Y‐tube bioassays confirmed attraction of maleO. fagito bursting beech buds and compound1. Attraction of maleO. fagito1, emitted in large quantities from American beech, is likely adaptive because both oviposition and mating ofO. fagicoincide with budburst. Our data suggest that traps baited with1may be useful for monitoring the spread ofO. fagiin North America.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".