The effects of nitrogen addition on dissolved carbon in boreal forest soils of northeastern China
Bibliographic record
Abstract
Abstract Understanding the effects of nitrogen (N) addition on dissolved carbon in boreal forest soils is essential for accurate evaluation of regional carbon balances. The objective of this study was to determine the effects of different levels and types of N addition on soil dissolved carbon concentration in a cold-temperate coniferous forest through an in-situ fertilization experiment. Simulated atmospheric N addition was applied in a factorial experiment with N addition level (control, 10, 20 and 40 kg of N ha−1yr−1) and N type (NH4Cl, KNO3 and NH4NO3) treatments. The experiment was conducted over the 2010 growing season (May-September) at the Kailaqi farm of Genhe Forestry Bureau, located in the northern Great Xin’an mountain range, northern China. Monthly N addition treatments were applied in three replicate plots per treatment (n = 36), and measurements of dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) were derived from monthly sampling of the organic and mineral soil horizons. There was a significant effect of N type, with the combined N source (NH4NO3) producing significantly higher DOC than the control (ambient addition) or the NH4Cl treatment in both the organic and mineral layers. The N addition treatment increased DIC in the organic layer at the low levels only, while N type did not have a significant effect. There was a significant interaction of the month and the N level treatment, as low level N addition tended to increase the content of soil DOC while high level N tended to inhibit soil DOC content, with these trends being most pronounced in the middle of the growing season. These results elucidate the importance of the type and timing of N additions to the dynamics of soil carbon pools.
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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".