Effects of artificial nitrogen deposition on the forest floor and soil chemistry in chestnut-leaved oak (<i>Quercus castaneifolia</i>) plantation in northern Iran
Bibliographic record
Abstract
Human demand for food and energy has led to significant changes in the level of reactive nitrogen (N) released to the atmosphere and then deposited in the biosphere. This study aimed to investigate the impact of the deposition of artificial N on the forest floor and on the soil chemical properties in an oak (Quercus castaneifolia C.A. Mey.) plantation in northern Iran. Twelve plots of 200 m2 (20 m × 10 m) were set up in the study area. Four N treatments were considered: zero (control), 50 (low), 100 (medium), and 150 (high) kg N·ha−1·year−1. N in the form of NH4NO3 solution was manually sprayed onto the understory plots monthly for 1 year. The total N, phosphorus (P), potassium (K), and organic carbon (OC) of the forest floor were measured. Soil N, available P, available K, pH, EC (electrical conductivity), OC, microbial biomass C (MBC), and urease enzyme activity were measured in the 0–10 cm depth. The concentration of total N and P of the forest floor was significantly higher in the high-N treatment. The total concentration of N (+36%), the urease activity (+44%), and EC (+12%) of soil increased with raising the high-N treatment compared to the control, but the MBC (−20%), available P (−28%), and available K (−15%) were significantly reduced in the high-N treatment. Our results were obtained with simulated deposition rates that exceed ambient fluxes, but ambient N deposition is nevertheless high in our study area.
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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".