Differential response by seedlings of three sub-boreal conifer species to high- and low-carbon wood ash amendment
Bibliographic record
Abstract
In Canada, there is a need to implement value-added uses for wood ash (hereafter ash) generated from bioenergy facilities as most ash is landfilled. Ash application to forests can provide benefit via nutrient supply, amelioration of soil acidity and, sometimes, increased tree growth. However, information is limited on the response of conifer species to different wood ash types applied to fine-textured soil typical of north-central B.C. We conducted a 16-month seedling pot trial that examined the response of Douglas-fir ( Pseudotsuga menziesii), lodgepole pine ( Pinus contorta var. latifolia), and hybrid white spruce ( Picea glauca × engelmannii) to high- (HCA) and low-carbon ashes (LCA) applied (up to 10 Mg mineral matter ha−1 equiv.), with and without fertilizer N (200 kg N ha−1 equiv.), to fine-textured forest soil. Pine and spruce exhibited a 1.6- and 1.4-fold increase in shoot biomass at the high rate of HCA with fertilizer N. At study end, the high rate of LCA had the greatest soil pH, EC and total K in the upper forest floor, but the HCA had greater total B, P and Zn. LCA elicited increased foliar B in pine, but HCA increased foliar Ca in spruce when co-applied with fertilizer N. In general, Douglas-fir growth did not respond to ash treatments, and seedling mortality was observed in some LCA treatments. Ash treatments helped offset some nutrient deficiencies induced by N fertilization. Ash type influenced soil chemical as well as seedling growth and nutrition responses.
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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".