Limited effect of wood ash application on soil quality as indicated by a multisite assessment of soil organic matter attributes
Bibliographic record
Abstract
Abstract In Canada, the combustion of forest biomass for bioenergy production has been increasing with an associated increase in residual wood ash. Wood ash is typically landfilled as waste but there is growing interest in applying wood ash to the soils of commercial forests. Ideally, wood ash supplies nutrients that may have been removed through biomass harvesting, increases soil pH, which improves nutrient availability, and potentially improves site productivity, but there is also potential for detrimental effects, such as toxicity, that impair soil functions. The objective of this study was to investigate the effects of wood ash application on soil organic matter attributes at eight experimental sites across Canada that are examining the effects of wood ash application on site fertility, productivity, and soil biodiversity. Wood ash application had an effect on total carbon (TC) and total nitrogen, microbial biomass carbon (MBC), hot water extractable carbon (HWEC), mineralizable C, sand size C, and HWEC and MBC normalized to TC, but changes were typically restricted to single sites or differed in their direction, that is, positive or negative. Based on the limited and inconsistent effects of ash on established indictors of soil quality measured in this study, there does not appear to be any advantageous or detrimental effects of adding wood ash to forest soil quality.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".