Restoring soil and tree nutrition through non‐industrial wood ash additions to sugarbushes
Bibliographic record
Abstract
Nutrient losses from forest soils caused by decades of acid deposition have affected tree growth and depleted soils of essential nutrients in eastern North America. Non‐industrial wood ash (NIWA) is rich in macronutrients and may be a potential remediation strategy to restore lost nutrients as a forest soil amendment. We evaluated the effects of a single NIWA application on forest soils and Sugar maple ( Acer saccharum Marsh) foliage at three sugar bush stands in Muskoka, Ontario, Canada. Soil pH and calcium (Ca) increased in the treatment plots (4 and 8 Mg/ha) 1 year after application and remained elevated into year 2. Soil potassium and magnesium concentrations also increased in the treatment plots; however, changes varied in intensity depending on the element, site, and time following application. Changes in soil metal concentrations after application were restricted to the organic soil horizons increasing in the litter in year 1 followed by a decrease in year 2 that was accompanied by increases in the fibrous‐humic layer in year 2. Base cation concentrations increased significantly in sapling and mature sugar maple foliage particularly in the mature foliage in year 2. Despite changes in soil metals, changes in foliar metal concentrations were generally not significant. Foliar Diagnosis and Recommendation Integrated System indices indicated deficiencies in Ca and nitrogen (N) suggesting Ca benefits take longer to appear and that supplementing with N additions on acidic soils exhibiting foliar deficiencies might prevent further imbalances from occurring while facilitating tree recovery.
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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 | 0.001 | 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".