Butternut (<i>Juglans cinerea</i>) health, hybridization, and recruitment in the northeastern United States
Bibliographic record
Abstract
Butternut (Juglans cinerea L.) trees are being extirpated from their natural range by an epidemic caused by a fungal pathogen. Widespread mortality is reminiscent of past epidemics on American chestnut (Castanea dentata (Marsh.) Borkh.) and American elm (Ulmus americana L.). Butternut has remained relatively understudied, resulting in unsampled areas and gaps in our understanding of this forest epidemic and the future outlook of this species in North America. The previously unsampled area consisting of the northeastern United States was surveyed for the presence of J. cinerea, and several population health metrics were recorded, including recruitment, disease pressure, and hybridization. A total of 252 butternut trees were sampled. Analysis indicates that there is insufficient J. cinerea recruitment to maintain population sizes. Further compounding low recruitment, butternut saplings demonstrate elevated levels of disease impact from the fungal pathogen Ophiognomonia clavigignenti-juglandacearum Broders & Boland. Natural hybridization of butternut with introduced congenics such as Juglans ailantifolia Carrière is strongly associated with lower disease impact. Hybrid trees displayed an average of 2.4 cankers per tree compared with 4.5 cankers for nonhybrid butternut. Further niche and resistance studies are required to assess whether butternut hybrids can replace butternut in a natural setting. It still remains uncertain whether tree size or habitat affect disease impact; however, smaller trees, often residing in riparian habitats, were found to have a greater number of cankers. The data presented here, combined with past studies, provide critical information for use in butternut management strategy plans.
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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) | 0.001 | 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".