Trace metal biogeochemical responses following wood ash addition in a northern hardwood forest
Bibliographic record
Abstract
Wood ash may be useful as a forest soil amendment in Canada, but trace metals can have detrimental effects if they accumulate in, or are transported from, forest ecosystems. Metal concentrations in soil water and sugar maple (Acer saccharum Marsh.) seedling tissue chemistry were measured in a north temperate hardwood forest over 4 years following a biomass boiler ash addition field trial. Twenty plots (3 m × 3 m) were established in Haliburton Forest with both fly and bottom ash treatments of 0, 4, and 8 Mg·ha−1 with four replicates, and tension lysimeters were positioned in each plot at 30, 50, and 100 cm depths. Over the 4 years, soil water metal concentrations in treated plots were not significantly different from those of the control plots. No differences in metal concentrations in foliage of sugar maple seedlings could be detected, but there were significantly higher concentrations of some metals (Al, Fe, Zn, Pb, Ni, and Sr) in roots of treated plots. Simulated drought mobilized several metals in upper mineral soil, but this mobilization occurred similarly in controls and ash-treated soils. These results suggest that doses below 8 Mg·ha−1 industrial wood ash with trace metal concentrations below Canadian regulatory limits do not cause an increase in trace metal mobility or availability in northern hardwood forests with acidic soils during the first 5 years after application.
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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".