Short‐term growth response of jack pine and spruce spp. to wood ash amendment across Canada
Bibliographic record
Abstract
Abstract Wood ash amendment to forest soils contributes to the sustainability of the growing bioenergy industry, not only through decreased wood ash waste disposal in landfills but also by increasing soil/site productivity and tree growth. However, tree growth studies to date have reported variable responses to wood ash, highlighting the need to identify proper application rates under various soil/site conditions to maximize their benefits. We explored the influence of tree species, wood ash nutrient application rates, time since application, stand development stage, and initial (i.e., before wood ash application) soil pH and N on short‐term tree growth response to wood ash amendment across eight unique study sites spanning five Canadian Provinces. Jack pine (Pinus banksianaLamb) had the most positive response to wood ash amendment compared to white (Picea glaucaMoench), hybrid (Picea engelmanniixglaucaParry), and black spruce (Picea marianaMiller), where increasing nutrient application rates increased height growth response. In comparison, black spruce had the most negative response to wood ash amendment, where increasing nutrient application rates slightly decreased height growth response. Site as a random effect explained additional variation, highlighting the importance of other unidentified site characteristics. By examining trends in short‐term growth response across multiple studies with variable site characteristics, we found growth response differed by tree species and nutrient application rates, and that jack pine is a promising candidate for wood ash amendment. These results contribute to our knowledge of optimal wood ash amendment practices and environmentally sustainable bioenergy production.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".