Foliar nematode, <i>Litylenchus crenatae</i> ssp. <i>mccannii</i>, population dynamics in leaves and buds of beech leaf disease‐affected trees in Canada and the US
Bibliographic record
Abstract
Abstract A foliar nematode, Litylenchus crenatae ssp. mccannii, is associated with beech leaf disease (BLD) symptoms. Information about the types of tissues parasitized and how nematode populations fluctuate in these tissues over time is needed to improve surveys as well as understand the nematodes role in BLD. During this study, the nematode was detected throughout the known range of BLD by researchers at both Canadian and US institutions using a modified pan method to extract nematodes. Monthly collections of symptomatic and asymptomatic leaves during the growing season (May–October), and leaves and buds between growing seasons (November–March), revealed that nematodes were present in all tissue types. Progressively larger numbers of nematodes were detected in symptomatic leaves from Ohio and Ontario, with the greatest detections at the end of the growing season. Smaller numbers of nematodes were detected in asymptomatic leaves from BLD‐infected trees, typically at the end of the growing season. The nematode was detected overwintering in buds and detached leaves. The discovery of small numbers of nematodes in detached leaves at one location before BLD was detected indicates that nematodes may have been present before disease symptoms were expressed. Other nematodes, Plectus and Aphelenchoides spp., were infrequently detected in small numbers. Our findings support the involvement of the nematode in BLD and indicate that symptoms develop only when certain requirements, such as infection of buds, are met. We also found that the nematode can be reliably detected in buds and leaves using the modified pan extraction method.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".