Macronutrients, metals, and metalloid concentrations in non-industrial wood ash in relation to provincial land application limits in Ontario, Canada
Bibliographic record
Abstract
Non-industrial wood ash (NIWA) provided by residents who heat with wood, is rich in base-cations and other nutrients and may be used as a forest soil amendment to return nutrients lost through acid deposition. However, due to concerns regarding high trace metal concentrations, most wood ash is landfilled in Canada. This study investigated the chemical variability of NIWA of individual samples and homogenized mixtures to determine if they met Ontario provincial trace metal restriction limits. One hundred and seven ash and 10 charcoal samples collected from residents of Muskoka, Ontario, and three 10-sample composites were analyzed. Chemical composition varied among individual samples, but nutrient levels were within or higher than reported ranges for industrial wood ash, while trace metal values were lower. Ninety-seven percent (104 of 107) of the samples were within Ontario Regulation 267/03 of the Nutrient Management Act, and after homogenization, all samples were below soil application restriction limits. This study indicates that NIWA can be safely used as a forest soil amendment but recommends routine testing of batch samples prior to application.
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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".