Long-term effects on soil-water chemistry of wood ash and nitrogen application in a conifer forest
Bibliographic record
Abstract
Wood-ash application to forestland has been proposed as a means to compensate for increased nutrient removal when harvesting logging residue in addition to stems. A study-plot experiment was established on a mineral soil site in Sweden to investigate how this measure affects soil-water chemistry. In 1995, 10 treatments were applied. Here, we present results from 9 to 17 years after application for eight of the treatments: control; 3 × 103, 6 × 103, and 9 × 103 kg·ha−1 of self-hardened and crushed wood ash (WA); 150 kg N·ha−1 supplied as ammonium nitrate; 3 × 103 kg WA and 150 kg N·ha−1 applied simultaneously; 3 × 103 kg WA with 150 kg N·ha−1 applied 1 month before the ash; and 3 × 103 kg·ha−1 of pelleted ash. Soil-water samples were collected from a depth of 50 cm. Treatment effects (p < 0.05) were detected in the electrical conductivity, pH, and concentrations of K+, Mg2+, Ca2+, Al, SO42−-S, and B. Elevation of K+ and SO42−-S concentrations tended to cease toward the end of the study period. Effects were generally more pronounced with increasing ash dosage. No difference was detected between the 150 kg N·ha−1 treatment and the control. Despite the high solubility of the ash, its effects on soil-water chemistry could still be detected 9–17 years after application.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".