Could a residential wood ash recycling programme be part of the solution to calcium decline in lakes and forests in Muskoka (Ontario, Canada)?
Bibliographic record
Abstract
One possible solution to the recent decline of calcium (Ca) concentrations in Canadian Shield forests and lakes in eastern North America is the addition of Ca-rich wood ash to watersheds. We investigated the feasibility of using small, mainly residential sources of non-industrial wood ash (NIWA) for this purpose by quantifying concentrations of its major nutrients and metals, its toxicity to Daphnia in aqueous extracts, and estimating the amount of NIWA available in the District of Muskoka in central Ontario. Locally collected NIWA averaged 30% Ca, and also contained smaller but significant amounts of K, Mg, Na, and P. Of these, K was so soluble that it was toxic to Daphnia over 48 h in the concentrate and 10-fold dilution; however, sedimented ash was not toxic over 15 d. Most metal levels in NIWA were below targets permitting unrestricted land application. However, Cu and Zn were just above these targets, but well below those for conditional use. Muskoka residents generate about 235 000 kg of NIWA annually, not enough to treat all central Ontario areas affected; however, a NIWA recycling programme implemented across southern Ontario could generate enough ash to solve the Ca decline problem in Muskoka’s forests and lakes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".