Decomposition and Nitrogen Mineralization from Biosolids and Other Organic Materials: Relationship with Initial Chemistry
Bibliographic record
Abstract
Biosolids are effective forest fertilizers. In order to facilitate their use it is important that one be able to predict the amount and rate of mineralization of nutrients, particularly nitrogen, and the relationship between substrate chemistry and N release. We examined the relationships between substrate quality and nitrogen release in a variety of organic materials. Rates of decomposition and net N mineralization from four biosolids, wheat straw, paper fines, and Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] needle litter were measured during 391-d incubations in a greenhouse, and at two field sites in wet coastal and dry interior forests. Decomposition rates were best predicted by a model incorporating the ratio of carbon to organic matter. The decomposition model extrapolated well to the field when site-specific correction factors were applied. There was a weak relationship between rates of decomposition and net N mineralization. Rates of net N mineralization were best predicted by a model incorporating the initial organic N concentration and the proportion of phenolic C determined from solid-state 13C nuclear magnetic resonance (NMR) spectroscopy. The mineralization model extrapolated less well to the field, but the effect of substrate chemistry was still apparent. Among the four biosolids there was a strong correlation between organic N concentration and indices or protein determined from 13C NMR, suggesting that these protein indices may be useful for predicting N mineralization from biosolids. There was some evidence that the protein content and N mineralization in biosolids may be predictable from the sewage treatment process employed.
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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 | 0.000 | 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".