The role of composting in recycling manure nutrients
Bibliographic record
Abstract
Recently, composting has been gaining increased attention as an alternative means of handling manure generated by the livestock industry. Composting is not a new technology, it merely controls what is a natural decomposition process. A major advantage of composting is reduced mass, volume and water content compared with fresh manure which in turn reduces transportation requirements. Concomitant benefits include elimination of pathogens, parasites, weed seeds and odour emissions on land application. However, carbon (C) and nitrogen (N) losses and greenhouse gas (GHG) emissions are associated with composting. Nutrients are stabilized during composting which slows their release once soil-applied. Compost also enhances soil physical and biological properties and has a disease suppression effect. Where the supply of manure currently exceeds land availability for application, or in some future scenario, if producers need to comply with stricter manure application rate regulations, composting may be an option to encourage nutrient export from high-loading watersheds to soils that may benefit from nutrient and organic matter inputs. Composting may be seen as a means of maximizing the potential for recycling manure nutrients by soils and crops while protecting surface and groundwater resources from manure-related contamination. Key words: Manure, compost, nutrients, cropping systems, soil quality
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".