Long-term effects of nitrogen fertilization on soil chemistry in three Scots pine stands in Sweden
Bibliographic record
Abstract
Adding nitrogen to coniferous forests on mineral soils will increase stem-wood growth in most boreal forests. The addition of nitrogen affects soils and waters as well. This investigation was conducted to evaluate the long-term effects of nitrogen fertilization at different intensities on soil chemistry in nitrogen-limited ecosystems. The study was performed at three experimental sites that were originally established around 1980 in Scots pine ( Pinus sylvestris L.) stands. Fertilization regimes with applications ranging from conceivable commercial rates to very intensive rates (3× 150 kg N·ha –1 up to 12× 150 kg N·ha –1 ) had been applied. Samples were collected from the FH horizon at all sites and 0–20 cm in the mineral soil at two sites and analyzed for pH and major nutrients. The carbon to nitrogen ratio in the FH horizon decreased with increasing total nitrogen application, while the concentrations and contents of nitrogen and exchangeable magnesium and phosphorus increased. The concentration and contents of exchangeable potassium decreased in both the FH horizon and the mineral soil. In general, larger effects on soil chemistry were observed with increasing fertilization intensity.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".