Precipitation of Liquid Swine Manure Phosphates Using Magnesium Smelting By‐Products
Bibliographic record
Abstract
Swine manure contains considerable amounts of total (P) and soluble phosphorus (PO(4)-P) which may increase the soil P content when applied in excess to crop requirements and, consequently, risk water eutrophication. The feasibility of using magnesium (Mg) from the by-product of electrolysis and foundries (BPEF) for the removal of P from liquid swine manure was studied by adding up to 3 g of Mg as BPEF per liter of nursery (NU) and grower-finisher (GF) swine manure in 25-L plastic buckets. Changes in P and other elements were monitored for up to 360 h. Small amounts of Mg as BPEF (0.5 and 1.0 g Mg L(-1) manure) reduced the total P concentration of the liquid fraction by 70 to 95% of both manure types with respect to the control treatment of mixed raw manure. A settling period of 8 h or more was necessary to significantly reduce the liquid fraction's total P concentration for both manure types. Reduction of PO(4)-P varied from 96 to 100% in the liquid fractions for both manure types, which along with natural settling, explains most of the total P reduction in that fraction. The addition of BPEF did not influence the N content of manure. The low P liquid fraction can be safely applied to saturated P soils whereas the high P solid fraction offers the opportunity of transporting manure to agricultural soils deficient in P. Since N is conserved, both liquid and solid fractions could be valuable fertilizer manure by-products.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".