Phosphorus Recycling from manure – A Case Study on the Circular Economy : Work package 4
Bibliographic record
Abstract
In this case study paper, phosphorus recycling from manure is discussed, with a special focus on the recycling process developed by the BioEcoSIM consortium.We analysed phosphorus flows globally, at a European level and on a national level, with a focus on the Netherlands.In the Netherlands, as in some other regions in Western Europe, there is excess supply of manure due to intensive livestock production.The oversupply, combined with legislation, generates a negative manure price.This negative manure price creates the business case for BioEcoSIM and other manure processing techniques.The BioEcoSIM technique processes manure into phosphorus and nitrogen fertilizer as well as an organic soil improver or biochar.By extracting from manure the useful components, transport costs are reduced.Furthermore, greenhouse gas emissions and particulate matter formation are decreased.However, since manure is already almost completely recycled on arable and pasture land, the effect on phosphorus flows is limited.The EU phosphorus flows show that the main losses of phosphorus in the food sector are through sewage sludge, other waste water and food waste, and not through manure.Nonetheless, losses of phosphate from manure do have a high environmental impact, since it causes eutrophication.This paper shows also what the macroeconomic consequences of phosphorus recycling from manure will be.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".