Contribution of atmospheric nitrogen deposition to net primary productivity in a northern hardwood forest
Bibliographic record
Abstract
Net primary productivity (NPP) in northern temperate forests is an important part of the global carbon cycle. Because NPP often is limited by nitrogen (N), atmospheric N deposition (Ndep) may increase forest NPP. At a northern hardwood forest site in northern Lower Michigan, USA, we quantified rates of N supply by Ndep, canopy retention of Ndep (Ncr), and soil net N mineralization (Nmin); calculated the N requirement of NPP; and estimated the fraction of NPP that could be attributed to atmospheric N inputs. Net N mineralization supplied 42.6 kg N·ha–1·year–1 (84% of the NPP N requirement), and Ndep averaged 7.5 kg N·ha–1·year–1 (15%). The forest canopy retained 38% of Ndep (Ncr = 2.8 kg N·ha–1·year–1), primarily in the forms of organic N and NH4-N. Fine root (62%) and leaf (31%) N requirements dominated the NPP N requirement of 50.7 kg N·ha–1·year–1. Annual N supply by the processes we measured agreed very closely with the NPP N requirement, suggesting that internally cycled N and N of atmospheric origin contribute to the N nutrition of this forest. Our results indicate that up to 15% of the NPP at this site may be driven by atmospheric N inputs.
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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 | 0.000 | 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".