Effects of simulated long-term N deposition on <i>Picea abies</i> and <i>Pinus sylvestris</i> growth in boreal forest
Bibliographic record
Abstract
Modelling studies have suggested that atmospheric nitrogen (N) deposition will increase forest carbon sequestration by stimulating tree growth. However, few long-term experiments studying N deposition effects on tree growth in boreal forests have been reported. This study empirically explores the relation between application rates of N, simulating levels of N deposition relevant for Europe, and the growth of Picea abies (L.) Karst. and Pinus sylvestris L. in a low N deposition area (<2 kg N·ha−1·year−1), we manually added 0, 12.5, and 50 kg N·ha−1·year−1 to a forest site dominated by P. abies for 19 years and 0, 3, 6, 12.5, and 50 kg N·ha−1·year−1 to a forest site dominated by P. sylvestris for 10 years. On both sites, linear regression analyses displayed significant relations between mean annual tree growth and N additions: P. abies relative growth rate increased by 1.2% per kg N added and that of P. sylvestris increased by 1.6% per kg N added. The growth response, however, varied over time, highlighting the necessity for long-term experiments to apprehend effects of eutrophication on forests. Generally, our study confirms the results of previous modelling studies assessing forest growth responses to N deposition over Europe.
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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) | 0.000 | 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".