A review of the influence of heat drying, alkaline treatment, and composting on biosolids characteristics and their impacts on nitrogen dynamics in biosolids-amended soils
Bibliographic record
Abstract
Application of biosolids to agricultural land has gained increasing attention due to their rich nutrient content. There are a variety of treatment processes for converting sewage sludge to biosolids. Different treatment processes can change the physicochemical properties of the raw sewage sludge and affect the dynamics of nutrient release in biosolids-amended soils. This paper reviews heat drying, alkaline treatment, and composting as biosolids treatment processes and discusses the effects of these treatments on biosolid nitrogen (N) content and availability. Most N in the biosolids remain in organic forms, regardless of biosolids treatment type but considerable variation exists in the mean values of total N and mineralizable N across different types of biosolids. The highest mean total N content was recorded in heat-dried biosolids (HDB) (4.92%), followed by composted biosolids (CB) (2.25%) and alkaline-treated biosolids (ATB) (2.14%). The mean mineralizable N value was similar between HDB and ATB, with a broader range of mineralizable N in ATB. The lowest N availability was observed in CB. Although many models have been extensively studied for predicting potential N mineralization in soils amended with organic amendments, limited research has attempted to model soil N mineralization following biosolids application. With biosolids being a popular, economical, and eco-friendly alternative to chemical N-fertilizers, understanding biosolids treatment effects on biosolids properties is important for developing a sound biosolids management system. Moreover, modeling N mineralization in biosolids-amended soils is essential for the adoption of sustainable farming practices that maximize the agronomic value of all types of biosolids.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".