Calcium addition at the Hubbard Brook Experimental Forest reduced winter injury to red spruce in a high-injury year
Bibliographic record
Abstract
Laboratory experiments have verified that acid-deposition-induced calcium (Ca) leaching reduces the foliar cold tolerance of red spruce (Picea rubens Sarg.) current-year foliage, increasing the risk of winter injury and crown deterioration. However, to date no studies have shown that ambient losses in soil Ca have resulted in increased winter injury in the field. In 2003, a year of severe region-wide winter injury to red spruce, we measured the nutrition and winter injury of current-year foliage and bud mortality for red spruce on two watersheds at the Hubbard Brook Experimental Forest in Thornton, New Hampshire: (1) a reference watershed that has undergone considerable Ca loss attributed to acid-deposition-induced leaching and (2) a watershed that was fertilized with CaSiO3in 1999 to replace lost Ca. For all crown classes combined, winter injury was significantly greater (P = 0.05) for red spruce on the reference watershed than for spruce on the Ca-addition watershed. Differences in foliar injury were particularly evident for dominant and codominant trees. For these crown classes, red spruce on the reference watershed lost about 75% of their current-year foliage to winter injury, about three times more than foliar losses for the Ca-addition watershed (P = 0.01). Patterns of bud mortality followed that of foliar injury. The only difference in foliar cation nutrition detected was a significantly greater concentration of Ca in red spruce foliage from the Ca-addition watershed relative to spruce from the reference watershed (P = 0.001). Differences in Ca concentration, foliar winter injury, and bud mortality that occurred coincident with watershed Ca treatment provide the first evidence that ambient Ca depletion is associated with elevated winter injury of red spruce trees.
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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 | 0.001 | 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".