Factors Influencing the Concentration of Volatile Fatty Acids, Ammonia, and Other Nutrients in Stored Liquid Pig Manure
Bibliographic record
Abstract
In order to minimize odor and manage nutrients in liquid pig manure we need to be able to predict what operational practices most influence the concentrations of volatile fatty acids (VFAs), ammonium nitrogen (NH(4)(+)-N), and other nutrients present in the manure. To determine this, we collected manure from 15 pig operations in southwestern Ontario in the fall of 2001 and 2002 and spring of 2002 and 2003. The manure was stored in concrete tanks at all operations. Manure from finishing pigs had the highest concentration of VFAs, NH(4)(+)-N, and other nutrients, followed by manure from mixed operations, and then manure from sow operations. The average concentration of total VFAs and NH(4)(+)-N in finishing pig manure was 166 mM compared with 36 and 99 mM, respectively, in sow manure. Total N, P, and K were 2.3, 2.5, and 1.7 times greater, respectively, in finishing pig compared with sow manure. There was no seasonal or year to year variation in amount. The diet of the pigs, use of feed additives or antibiotics, location of tanks, and whether the tanks were covered or mixed were not significant factors contributing to the difference in manure chemistry. The main reason for the differences between the three types of manure was manure dilution. The average dry matter content of finishing pig manure was 4.5 times that of sow manure. This was due to larger density of pigs in finishing compared with sow operations, less manure storage capacity per pig for finishing compared with sow operations, and more wash water being used for sow operations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".