Soil-solution chemistry in a coniferous stand after adding wood ash and nitrogen
Bibliographic record
Abstract
Wood-ash applications have been proposed to promote the long-term sustainability of forest production at increased harvest intensities. Effects of wood-ash and nitrogen (N) application on soil-solution chemistry were studied for 9 years following application in a coniferous stand in Sweden. Crushed, self-hardened wood ash was applied at 3, 6, and 9 Mg·ha1 alone, the lowest dosage both with and without 150 kg N·ha1. Pelleted wood ash (3 Mg·ha1) and N were also applied alone. The soil solution was sampled by suction cups at 50 cm depth. The crushed, self-hardened ash readily dissolved in water, as reflected in increased soil-solution concentrations of sodium and sulphate. Significant (p < 0.05) elevations were also found for potassium, calcium, aluminum, and total organic carbon. Vanadium, chromium, manganese, nickel, copper, zinc, arsenic, and lead were not significantly affected by the ash treatments, but cadmium tended to increase in the treatments with ash alone. From the fourth year onwards, the pH of the soil solution was lowered and the aluminum concentration raised in the plots given 9 Mg crushed ash·ha1. Fertilization with N alone temporarily increased concentrations of inorganic N, cadmium, aluminum, and zinc and decreased the pH. The crushed ash generally had longer lasting effects than N fertilization.
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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 | 0.001 | 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) | 0.001 | 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".