DIVISION S-7-FOREST & RANGE SOILS Influence of Edaphic Factors on Sugar Maple Nutrition and Health on the Allegheny Plateau
Bibliographic record
Abstract
ABSTRACT nce County, Wisconsin was the first to receive systematic study (Giese et al., 1964; Westing, 1966; Millers et al.,Sugar maple (Acer saccharum Marsh.) decline has been a problem 1989). Since then, well-documented sugar maple de-on the Allegheny Plateau for the last two decades. Previous work found that sugar maple is predisposed to decline by poor nutrition clines have occurred in Massachusetts in the 1960s and incited to decline by severe insect defoliation. Nutritional diagno- (Mader and Thompson, 1969), Ontario in the 1970s ses have been based on foliar chemistry; there is little information on (Hendershot and Jones, 1989; Gross, 1991), Quebec, soil attributes that influence susceptibility. We evaluated relationships New York, and Vermont in the 1980s (Bernier and among soil characteristics, foliar chemistry, and sugar maple decline Brazeau, 1988a,b,c; Kelley, 1988; Bernier et al., 1989; for 43 stands on the Allegheny Plateau in New York and Pennsylvania Hendershot and Jones, 1989; Bauce and Allen, 1992; using correlation and stepwise regression techniques. Foliar Ca and Cote et al., 1995; Ouimet and Camire, 1995; Wilmot etMg concentrations correlated with soil exchangeable cations ex-al., 1995), and Pennsylvania in the 1980s and 1990s (Kolbpressed on a concentration or site capital basis. Expression of base and McCormick, 1993; Long et al., 1997; Horsley et al.,cation availability as a saturation value, or in ratio with Al, slightly 2000). Stress events such as defoliations, droughts, andimproved the relationships, suggesting that antagonistic cations are important to sugar maple nutrition. The best predictions of foliar extreme weather events (late spring frosts, mid-winter
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.017 | 0.002 |
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".