Why and when can biosolids be used as a soil amendment for ecosystem reclamation and rehabilitation?
Bibliographic record
Abstract
Biosolids are a source of nutrient-rich organic material that can be used to improve degraded or disturbed soils. However, public perception of the use of biosolids on land is both positive and negative and can change over time and be different in different regions of the world. Research on the land application of biosolids has increased in the past 20 years, but there is little consensus on how the environment responds to biosolids applications. Here, I (1) present public perception research on the use of biosolids in land application in British Columbia, Canada, (2) present a review of the literature on the effects of biosolids in land application with a particular focus on plant community development, and (3) provide recommendations for the use of biosolids in land application depending on potential differences in ecosystem reclamation goals. In the public perception research, many citizens see the value in the use of biosolids as a sustainable fertilizer, especially in mine reclamation, but some have expressed concerns about pathogens in biosolids and their effect on humans and animals. The literature review revealed that biosolids increase plant productivity but have no effect on plant diversity. The research suggests that climatic conditions and seeding are influential in altering ecosystem and community level responses to biosolids application.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".