Relationships between pest density and associated leaf necrosis for an invasive leaf-mining weevil, <i>Orchestes fagi</i>, on American beech (<i>Fagus grandifolia</i>)
Bibliographic record
Abstract
Pest density – plant damage relationships are essential guides for decision-making in integrated pest management. In this article, we established pest density – leaf damage relationships for the beech leaf-mining weevil, Orchestes fagi L. (formerly Rhynchaenus fagi, Coleoptera: Curculionidae), in its invasive range of Nova Scotia, Canada. Outbreaks of O. fagi cause tree-wide leaf necrosis in American beech (Fagus grandifolia Ehrh.), which can eventually result in tree mortality. In 2014 and 2016, we collected weekly samples in stands with American beech and assessed leaves for densities during different life stages (eggs, larvae, and pupae), population proxy measures (adult feeding damage, egg slits, and larval galleries), and percent necrosis. In general, feeding damage and leaf necrosis plateaued soon after the end of budburst, but before the larval mine expanded. This strongly suggested that leaf necrosis may be linked to damage caused by adults or by mine initiation rather than that caused by larval mine expansion and gallery development. The density of O. fagi per leaf for life stages and population proxies all significantly explained ∼42%–81% of the variation in end-of-season percent leaf necrosis. Results from this study provide a variety of relationships that could be used in both short- and long-term monitoring efforts for O. fagi.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".