Early response of understory vegetation to wood ash fertilization in the sub boreal climatic zone of British Columbia
Bibliographic record
Abstract
Wood ash can be used as a soil amendment in forest ecosystems to alleviate nutrient loss, ameliorate soil acidity, increase tree growth, and reduce landfilled waste. Two hybrid spruce (Picea glauca X engelmannii) plantations in interior British Columbia were treated with two types of bioenergy-produced wood ash (high carbon boiler ash and low carbon gasifier ash) with or without nitrogen fertilizer in a two-way factorial block design. Ash and nitrogen treatments were applied to 8.0 m radius plots at a rate of 5000 kg ha-1 loose ash (dry basis), and 100 kg N ha-1 of urea in pellet form. Changes in understory vegetation cover were observed. There was a significant (p<0.05) effect of nitrogen and wood ash plus nitrogen application on understory vegetation community composition, with nitrogen application having the greatest effect. Discriminant function analysis indicated a differential response of species group to ash/ nitrogen treatments, though the effect size was small. We conclude that short-term changes to understory vegetation are minimal when these two ashes were applied at a rate of 5000 kg ha-1. Continued monitoring will determine if any long-term effects become apparent with time.
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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 | 0.000 | 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".