Urea fertilizations of a Norway spruce stand: effects on nitrogen in soil water and field-layer vegetation after final felling
Bibliographic record
Abstract
Effects of previous fertilization with N (in total, 600 kg urea-N·ha1 applied in 1976, 1980, and 1985) were studied after final felling in 1992 of a Norway spruce (Picea abies (L.) Karst.) stand in southern Sweden. The logging residues were removed from the site. In the clearcut, soil water at 50 cm depth was sampled 16 times with ceramic suction samplers (P80) in experimental plots during 19921995. The biomass and N content of the field layer was measured on seven occasions. The N storage of the field layer was significantly (p < 0.05) higher in the urea treatment than in the control. Significant interactions between treatment and time were found in soil water for nitrate-N and total N but not for ammonium-N, organic N, and pH. During the first year after final felling, nitrate-N tended to increase faster in the urea treatment than in the control. After a period with similar concentrations in both treatments, nitrate-N in the urea treatment declined while at the same time, a peak was observed in the control showing four to seven times higher concentrations than in the urea treatment. At the end of the study, the concentrations still appeared to be highest in the control. Thus, the study demonstrated the importance of using a sufficiently long study period when investigating environmental effects. Total leaching of nitrate-N from the urea treatment was roughly 40% ([Formula: see text]20 kg·ha1) less than that from the control. The difference in leaching may be partly explained by the greater accumulation of N in the field-layer vegetation in the urea treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".