Nitrogen and calcium additions increase forest growth in northeastern USA spruce–fir forests
Bibliographic record
Abstract
We determined responses of red spruce ( Picea rubens Sarg.) – balsam fir ( Abies balsamea (L.) Mill.) forests to 6 years of nitrogen (N), calcium (Ca), and N + Ca treatments (100, 160, and 260 kg·ha–1·year–1of N, Ca, and N + Ca, respectively) in New York (NY) and New Hampshire (NH). Forest responses to Ca treatments were also determined in Vermont (VT). Nitrogen treatments increased aboveground net primary production (ANPP) by 33% and 25% above controls in NY and NH, respectively. Similarly, N + Ca treatments increased ANPP by 27% and 28% in NY and NH, respectively. Calcium treatments increased ANPP by 25% and 21% above controls in NY and VT. Calcium treatment did not increase ANPP in NH, suggesting N, but not Ca limitation. Leaf-litter quantity and quality, and soil C and N storage were greater in treated than in control plots. Fine-root mass and production did not differ among treatments. Trees, therefore, assimilated more soil nutrients without increasing root growth in treated plots. Red spruce ANPP, however, declined or remained unchanged in response to N and Ca additions. The equivalent of 68–102 years of anthropogenic N addition to soils changed forest species composition without decreasing ANPP, and Ca additions did not prevent this change.
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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) | 0.001 | 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".