Effects of nitrogen additions on nitrogen resorption and use efficiencies and foliar litterfall of six tree species in a mixed birch and poplar forest, northeastern China
Bibliographic record
Abstract
We studied nitrogen (N) resorption efficiencies (NRE), N use efficiencies (NUE), carbon-to-nitrogen ratios of green (C:Ngreen) and senesced (C:Nsenesced) foliage, and foliar litterfall for six tree species under three N treatments (control, no N addition; low N addition, 2.5 g N·m–2·year–1; and high N addition, 5.0 g N·m–2·year–1) in a mixed birch and poplar forest in northeastern China in 2007 and 2008. N additions were initiated in 2006. NRE, NUE, C:Ngreen, and C:Nsenesced were significantly decreased by N additions and tended to decrease with increasing N addition treatments. N additions significantly increased foliar litterfall of Acer mono Maxim., Betula platyphylla Sukatschev, Pinus koraiensis Siebold & Zucc., and Populus davidiana Dode and slightly altered litterfall of Fraxinus mandschurica Rupr. and Populus koreana Rehder. High N addition changed foliar litterfall of A. mono, F. mandschurica, P. davidiana, and P. koreana more than low N addition, whereas an opposite pattern was found for B. platyphylla and P. koraiensis. Our study showed that foliar litterfall responses to N additions varied among tree species, but this could not be predicted by the interspecific differences in NRE, NUE, C:Ngreen, and C:Nsenesced under each of the three N treatments.
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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 | 0.001 | 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) | 0.000 | 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".