Impact of single and combined soil amendments on the growth and foliar nutrients of white spruce <i>(Picea glauca)</i> on a poorly regenerated logged site
Bibliographic record
Abstract
Regeneration failure is occasionally encountered in the boreal mixed forest following clear-cutting, primarily due to competing vegetation and altered soil conditions. This study investigates the effects of applying several soil amendments to improve white spruce plantation growth on poorly regenerated forest sites. Biochar (2.6 Mg ha−1), wood ash (7 Mg ha−1), and manure (105 Mg ha−1) were used alone or in combination, with effects on foliar elements and seedling growth assessed after two growing seasons. While biochar and wood ash have been frequently used, combining them with manure has been limited in boreal forests. Using a randomized complete block design, we measured soil pH, incident light, seedling growth, specific leaf area, and foliar nutrition. Manure significantly increased seedling growth (+37%) compared to treatments without it. It also increased foliar nitrogen (+17%) and phosphorus (+14%). Wood ash increased foliar nitrogen (+7%), phosphorus (+15%), potassium (+19%), and calcium (+29%). Biochar, without wood ash, decreased foliar aluminum by 56%. We conclude that manure represented an important nitrogen and phosphorus source for seedling growth. This research highlights the potential of amendment combinations for improving growth and foliar nutrition of seedlings in poorly regenerated boreal forest ecosystems, for example, where herbicide use is prohibited.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".