Soil-Based Treatment of Partially Treated Liquid Swine Manure
Bibliographic record
Abstract
A soil-column system was tested for the removal of soluble organics and nutrients from partially treated liquid swine manure. The liquid manure was applied to the 900 mm deep (300 mm of local topsoil and 600 mm of local subsoil) soil columns continuously for an eight-week period, and leachate as well as soil samples were analysed. An effective liquid manure application rate of 17 mm d(-1) was determined based on a preliminary liquid manure soil-based treatment experiment. It was found that more than 90% of five-day biochemical oxygen demand, chemical oxygen demand, total Kjeldahl and ammonia nitrogen, and total phosphorus could be effectively removed from the liquid manure by the soil system. Nitrogen contents accumulated in the soil matrix mostly within the 0 to 300 mm depth, while no significant increase was observed in sub soils. Soil analyses indicated the occurrence of nitrification and denitrification in the soil columns. Nitrogen balance showed that about 42% of the applied nitrogen was lost from the system during the liquid manure soil-based treatment experiment, suggesting the emission of ammonia and other gaseous nitrogen generated through nitrification and denitrification. The leachate of the soil treatment system was used to irrigate Bermuda grass. No negative effect of leachate was observed on the plant growth.
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) | 0.001 | 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".