Factors associated with the decline disease of sugar maple on the Allegheny Plateau
Bibliographic record
Abstract
Mortality of sugar maple (Acer saccharum Marsh.) has reached unusually high levels across northern Pennsylvania since the early to mid-1980s. We evaluated the influence of glaciation, topographic position, foliage chemistry, defoliation history, and stand characteristics (species composition, structure, density) on the health of sugar maple in 43 stands at 19 sites on the northern Allegheny Plateau. Using percent dead sugar maple basal area as the measure of health, we found that all moderately to severely declining stands were on unglaciated summits, shoulders, or upper backslopes. Stands on glaciated sites and unglaciated lower topographic positions were not declining. The most important factors associated with sugar maple health were foliar levels of Mg and Mn and defoliation history. The lowest foliar Mg, highest foliar Mn, and highest number and severity of insect defoliations were associated with unglaciated summits, shoulders, and upper backslopes. Declining stands had less than ~700 mg·kg -1 Mg and two or more moderate to severe defoliations in the past 10 years; both conditions were associated with moderately to severely declining stands. The decline disease of sugar maple seems to result from an interaction between Mg (and perhaps Mn) nutrition and stress caused by defoliation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".