Short-term changes in spruce foliar nutrients and soil properties in response to wood ash application in the sub-boreal climate zone of British Columbia
Bibliographic record
Abstract
Utilization of wood ash as a forest soil amendment in British Columbia could have numerous benefits, including potentially increasing tree growth. Two wood-derived bottom ashes (low-carbon gasifier ash and high-carbon boiler ash) were applied at 5000 kg ha−1 (dry weight equivalent) with urea (100 kg N ha−1), in a two-way factorial randomized block design across two (18- and 24-yr-old) hybrid spruce (Picea glauca × engelmannii Parry × Engelm.) plantations in interior British Columbia. Changes in spruce foliar nutrients and selected soil properties were evaluated. After one growing season, foliar Ca and S significantly increased in plots treated with ash, and foliar N significantly increased while foliar Mg and P significantly decreased in spruce treated with urea. In LFH horizons, soil pH, exchangeable base cations (Ca, Mg, and K), strong acid-extractable B, and Ca significantly increased (p < 0.05) with ash addition; soil pH was greatest in plots treated with urea plus gasifier ash. No significant treatment effects were observed in underlying mineral soil. We conclude that gasifier and boiler ash derived from clean wood applied at 5000 kg ha−1 are effective soil amendments as expressed within 1 yr by changes in nutrient status of LFH horizons and spruce foliage.
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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 | 0.001 | 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".