Long-term calcium addition increases growth release, wound closure, and health of sugar maple (<i>Acer saccharum)</i>trees at the Hubbard Brook Experimental Forest
Bibliographic record
Abstract
We surveyed and wounded forest-grown sugar maple ( Acer saccharum Marsh.) trees in a long-term, replicated Ca manipulation study at the Hubbard Brook Experimental Forest in New Hampshire, USA. Plots received applications of Ca (to boost Ca availability above depleted ambient levels) or Al (to compete with Ca uptake and further reduce Ca availability). We found significantly greater total foliar and membrane-associated Ca in foliage of trees in plots fertilized with Ca when compared with trees from Al-addition and control plots (P = 0.005). Coinciding with foliar Ca differences, trees exhibited a significant difference in crown vigor and in percent branch dieback among treatments (P < 0.05), with a trend towards improved canopy health as Ca levels increased. Annual basal area increment growth for the years following treatment initiation (1998–2004) was significantly greater in trees subjected to Ca addition compared with trees in control and Al treatments. Treatment-related improvements in growth were particularly evident after overstory release following a 1998 ice storm. The amount of wound closure was also greatest for trees in Ca-addition plots relative to Al-addition and control plots (P = 0.041). These findings support evidence that ambient Ca depletion is an important limiting factor regarding sugar maple health and highlight the influence of Ca on wound closure and growth following release from competition.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".