Uptake and partitioning of simulated atmospheric N inputs in<i>Populus tremuloides – Pinus strobus</i>forest mesocosms
Bibliographic record
Abstract
Canopies of forest ecosystems frequently intercept and retain >50% of atmospheric nitrogen (N) deposition. Atmospheric N reaching the forest floor often is retained in litter and soil pools, but if atmospheric N retained by the canopy is assimilated through foliar uptake, then N inputs may have greater effects on tree N requirements and growth than suggested by N additions to forest floors alone. To quantify foliar and root uptake of N deposition by trees and to determine patterns of within-tree N allocation, we performed a tightly controlled15N tracer experiment using constructed, seminatural forest mesocosms. Mesocosms contained seedling trees, which assimilated modest amounts of the aqueous15NH4Cl applied to their canopies and surrounding soils (10% and 14%, respectively). The seedlings allocated most of the15N that they took up to leaf biomass (40%–50%), regardless of uptake pathway. Soil pools retained >80% of the15N applied to mesocosm forest floors, with soil organic matter concentration explaining 79% of the variation in soil15N recovery across sampling depths. The low value for15N movement into biomass and its retention in soil pools suggests that atmospheric N deposition measured at a nearby forest site has no short-term effects on tree N content or growth.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".