Comparison of Three Digestion Methods For the Recovery of 17 Plant Essential Nutrients And Trace Elements from Six Composts
Bibliographic record
Abstract
A comparative investigation for recovery of 17 elements by using three digestion methods (nitric acid, nitric/perchloric acid and dry ashing) in six mature composts was conducted. The elements measured were Ca, Fe, K, Mg, Na, As, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Se and Zn. Two of the composts were prepared from mixtures of racetrack manure and sewage sludge. Another compost was developed from a mixture of food waste and straw with the addition of nitrogen fertilizer. The remaining three were MSW (municipal solid waste) composts from the Lunenburg Regional Recycling and Composting Facility, Nova Scotia, prepared in 1995, 1996 and 1997, matured, and used in the subsequent year. There were no significant differences between the three digestion procedures in the recovery of Ca, K, Na, and Hg. HNO3 digestion provided the highest recovery of As, Mo, Ni and Se, HNO3/HClO4 provided the highest recovery of Co, while dry ashing provided the highest recovery of Mn. For Cd and Pb, both HNO3 and HNO3/HClO4 provided similar and better recovery than dry ashing. For Cr, higher values were obtained by using dry ashing or HNO3/HClO4 digestion, while for Mg, HNO3 and dry ashing provided similar and better recovery than the HNO3/HClO4 digestion procedure. For Cu and Zn, HNO3 was superior to HNO3/HClO4 digestion, while dry ashing was not significantly different from the other two procedures. For Fe, dry ashing provided better recovery than HNO3. Element recovery depended not only on the digestion procedure, but also on the type of compost. Overnight presoaking of the compost samples in concentrated nitric acid at room temperature prior to heating did not improve the element recovery. The results show that when comparing the three methods, nitric acid digestion is sufficient for the recovery of the greatest number of elements in various composts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".