Diversity and potential sources of introduction of the beech leaf nematode ( <i>Litylenchus crenatae mccannii</i> ) to Ontario, Canada
Bibliographic record
Abstract
Litylenchus crenatae mccannii is a foliar nematode that causes severe damage to beech, known as beech leaf disease (BLD). Previous studies have characterized and identified L. crenataein Japan and a subspecies L. crenatae mccannii in the USA. Litylenchus crenatae mccannii has been found to cause BLD in native and non-native beech hosts in North America. As little is known about the distribution and diversity of the nematode in Ontario, Canada, beech foliage was sampled throughout southcentral Ontario. Incidence and severity maps show that BLD spread from its first detections in 2017, in Elgin and Norfolk counties, to the west and northeast. A gradient in severity and incidence was observed with the highest ratings at the locations where it was originally found. The extent of the nematode’s distribution is much broader than BLD, occurring throughout much of the range of American beech. Sequences of two barcoding loci for the L. crenatae mccannii isolates from 11 locations in Ontario showed 100% similarity within and among these locations. The genotype of L. crenatae mccannii found in Ontario is the same as that identified in the USA. This finding is consistent with the fact that 92% of beech imported into Canada is from the USA and suggests that the USA could be the origin of L. crenatae mccannii nematodes in Ontario or that both countries have imported beech trees from the same source country of BLD. This research provides motivation for further studies to clarify the sources of L. crenatae mccannii.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".