Factors Influencing the Regional Dynamics of Butternut Canker
Bibliographic record
Abstract
Butternut (Juglans cinerea) is an important component of native biodiversity in eastern North America. Of urgent concern is the survival of butternut, whose populations are declining rapidly, in large part due to an exotic pathogen, Ophiognomonia clavigignenti-juglandacearum, that causes butternut canker. The disease presently occurs throughout the range of butternut in North America, causing branch and stem cankers, dieback, and tree mortality. Despite the existential threat posed by O. clavigignenti-juglandacearum to butternut, a detailed understanding of the factors that drive cross-scale disease patterns is lacking. Therefore, we investigated the association of a range of factors, including tree attributes, topography, and weather, with butternut canker spatial dynamics at different scales using data collected in the province of Quebec, Canada. Trunk canker damage and dieback showed distinct geographic patterns. Bark phenotype was not significantly associated with trunk canker damage. Results suggest that open or dominant trees may show less dieback than intermediate or suppressed trees. Probability of the presence of trunk canker and percent dieback were proportional to the tree diameter at breast height. Temperature was positively associated with disease severity at a 1-km2scale. Our results provide strong evidence that multiple factors, notably weather, influence butternut canker epidemiology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".