Precipitating swine manure phosphorous using fine limestone dust
Bibliographic record
Abstract
Precipitating swine manure phosphorous using fine limestone dust. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 46: 6.1 6.6. The effectiveness of fine limestone dust in precipitating swine manure total phosphorous (TP) and total solids (TS) was measured using 3-L volumes and a 1.30 m volume. Respective duplicate limestone samples with particle sizes of 10, 17, 30, 12, 21, and 30 :m, (PULPRO 10, 17, and 30 and SW 12, 21, and 30) were mixed at levels of 0 and 6% into swine manure with a TS and TP of 7.4% and 1210mg/L, respectively. SW 21 was also mixed into the same swine manure at a 2% level. The 1.30 m volume of 8.3% TS swine manure was treated with 6% PULPRO 30. The depth of sludge precipitation was monitored over time and the supernatant and sludge were analyzed for TP, TS, and pH after 12 and 30 days for the 3-L and 1.30 m volumes, respectively. With 2 and 6% SW21, the supernatant liquid was analyzed a second time for TP after 60 days of settling. All limestone particle sizes and dosages performed similarly, by precipitating as much as 96 and 90% of the TP and TS into a sludge equivalent to 53% of the manure volume. Letting the supernatant settle for 60 days rather than 12 days further reduced its TP content by 40%. The 1.30 m volume test further demonstrated that the sludge could be pumped despite its TS and density of 22% and 1.07 kg/L, respectively. The addition of 2% limestone improved TP and TS precipitation by 4% and greatly improved sludge density and sludge separation from the supernatant.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".