Wood/sludge ash effects on white spruce seedling growth
Bibliographic record
Abstract
The disposal of wood ash and/or pulp and paper mill sludges is becoming increasingly more challenging as landfills are more difficult to site due to diminishing land availability and public opposition, as well as more costly to construct and operate because of increasingly stringent regulations. The most notable alternative to disposal that is receiving the attention of the forest industry is land application. The objective of this study, therefore, was to determine the influence of applying a pulp mill wood/sludge ash mixture on 1) various soil chemical properties of an Orthic Gray Luvisol and 2) the growth and nutrient uptake of white spruce [Picea glauca (Moench) Voss] seedlings. The pulp mill wood/sludge ash mixture from an olivine burner was surface broadcast at rates of 1 and 5 Mg ha -1 around white spruce seedlings planted on disc-trenched microsites. Soil solutions from lysimeters and soil samples were analyzed for exchangeable elements, soil pH and electrical conductivity while white spruce tissue samples were analyzed for various elements. Applications of pulp mill wood/sludge ash material significantly increased the soil pH from 4.8 to 6.9, resulting in increased soil extractable and soil solution P, Ca, and Mg. Soil electrical conductivity (EC) was increased from about 0.02 dS m -1 to approximately 0.10 dS m -1 by the heavier application rate of ash. In addition, at higher ash application rates, white spruce seedling growth was significantly decreased, suggesting salt phytotoxicity effects from the ash. These results indicate that with proper rate determination and management of salt effects, land application of this material would not appear to pose serious problems for white spruce seedling establishment. Key words: Picea glauca, wood/sludge ash, Luvisols, lysimeters, electrical conductivity, pH
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".