Short-term effects of wood ash application on soil properties, growth, and foliar nutrition of <i>Picea mariana</i> and <i>Picea glauca</i> seedlings in a plantation trial
Bibliographic record
Abstract
Burning biomass for energy generates ash that could be applied as a soil amendment to ameliorate acidity and mitigate nutrient losses associated with biomass harvesting. These soil improvements may also enhance tree growth and foliar nutrition. In this study, we applied low- and high-carbon wood-derived ash at rates of 0 (control), 1000, and 10 000 kg·ha−1 (dry weight equivalents) to soils planted with Picea mariana (Mill.) B.S.P. and Picea glauca (Moench) Voss in a replicated (5) factorial design. We measured soil properties, tree seedling height, and foliar nutrient contents prior to and 4 mo after wood ash addition to determine the immediate effects on soil physical, chemical, and biological properties, and tree seedling performance. We conclude that there were no negative effects of applying either wood ash and that application of ash at 10 000 kg·ha−1, particularly with the low-carbon ash, produced the greatest changes. We anticipate that changes may become more evident over the longer term, especially with respect to tree growth and nutritional responses (e.g., as nutrient uptake demand increases) once the seedlings become more established.
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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 | 0.001 |
| 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".