Hitching a ride: firewood as a potential pathway for range expansion of an exotic beech leaf-mining weevil, <i>Orchestes fagi</i> (Coleoptera: Curculionidae)
Bibliographic record
Abstract
Abstract We investigated the potential for human-mediated range expansion of an exotic beech leaf-mining weevil, Orchestes fagi (Linnaeus) (Coleoptera: Curculionidae: Curculioninae: Rhamphini) (formerly known as Rhynchaenus fagi) on timber or firewood, which for eight to nine months of the year may harbour adults in diapause. In both relatively low-density and high-density populations, adults were found on the base, middle, and upper boles of the primary host, American beech (Fagus grandifolia Ehrhart; Fagaceae), as well as red maple (Acer rubrum Linnaeus; Sapindaceae) and red spruce (Picea rubens Sargent; Pinaceae) in the vicinity. Comparatively few individuals were found on tree branches, or in the moss, duff, or soil collected beneath beech trees. Overwintering adults appeared to favour parts of trees with relatively high bark roughness. Our study suggests that, between the months of July through May, any woody stems near areas having O. fagi outbreaks are likely to harbour adults. Moreover, as all of the trees studied are common sources of timber or firewood, the harvest and transport of wood from these areas may facilitate outbreak spread; this may explain the multiple, distantly distributed populations of O. fagi that have been reported in eastern Nova Scotia, Canada in recent years.
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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) | 0.002 | 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".