Sugar maple sap, soil, and foliar chemistry in response to non-industrial wood ash fertilizer in Muskoka, Ontario
Bibliographic record
Abstract
Non-industrial wood ash may be an effective forest soil nutrient supplement but its use in Canada is largely restricted because of unknown concentrations of trace metal contaminants. Sugar maple ( Acer saccharum Marshall) is particularly sensitive to low soil calcium (Ca) levels, and though maple syrup is of great economic importance in Canada, it is unknown how wood ash could affect sap chemistry. Non-industrial wood ash (NIWA; 6 Mg·ha−1) applied to experimental plots in Muskoka, Ontario was rich in Ca (27%), while metal concentrations were well below provincial regulatory limits. One-year post-application, significant increases were observed in the treated plots in the soil pH and base cations (Ca, K, and Mg) in the surface soil horizons, and metal concentrations in the litter. Sap yield in the control plots was significantly lower in the first-year post-application than in the second year, but no other differences were found. In both tapping years, sap sweetness remained similar and differences in nutrient and metal concentrations between treatments were generally small and inconsistent. Foliar chemistry remained largely unchanged 1 year following application, except for K that was twice as high in the treated plots. Ultimately, NIWA is unlikely to significantly alter sugar maple sap chemistry, indicating that it is a viable nutrient supplement that can enhance soil fertility in sugar bushes with no impact on sap sweetness.
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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".