Influence of nutrition and stress on sugar maple at a regional scale
Bibliographic record
Abstract
Sugar maple (Acer saccharum Marsh.) decline disease on the Allegheny Plateau (region 1) resulted in high levels of mortality during the 1990s. Sugar maple was predisposed to decline because of an imbalance in Mg, Ca, and Mn nutrition and incited to decline by repeated defoliation. We sampled 33 stands in New York, Vermont, and New Hampshire (region 2) to determine if this model of sugar maple decline applies to a broader region. Low Ca and Mg and higher Mn levels were correlated with poorer tree health in both regions, but region 2 stands had little defoliation and few dead trees, suggesting that both unbalanced nutrition and stress are required for mortality to occur. We predict that stands with low foliar Ca and Mg and high Mn levels would incur increased mortality if stressed. In region 2, relationships between Ca, Mg, and Mn levels and dieback suggested that impacts on sugar maple may be caused by nutritional imbalance alone. Partial correlation analysis suggests that antagonism between Mg and Mn is the most important nutritional factor in region 1, while Mn supply is most important in region 2. We suggest that more research is needed on the interacting roles played by Ca, Mg, Al, and Mn in sugar maple performance.
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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) | 0.000 | 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 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".