Important Factors Affecting Biosolid Nitrogen Mineralization in Soils
Bibliographic record
Abstract
Abstract Biosolid nitrogen (N) ammonification, followed by nitrification in soil, produces nitrate ( ), which is not only a plant nutrient, but also a contaminant for ground water. Determining the most relevant factors influencing mineralization will help to manage N in biosolid-treated soils. Biosolid application rate, biosolid carbon (C):N ratio, biosolid organic N content, biosolid type, soil organic N content, soil pH, temperature, and time were compared among 12 published studies. Biosolid application rate, biosolid C:N ratio, and temperature significantly affected the mineralization rate and accounted up to 87% of the final model variability (R 2 = 79.1). Although the partial coefficients for soil pH and a dummy variable for biosolid were significant in the final model, their total contribution to the sum of partial coefficients was low (13%). There is insufficient evidence to conclude that soil organic N content and time contribute to biosolid N mineralization variability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".