Factors Influencing the Concentration of Volatile Fatty Acids, Ammonia, and Other Nutrients in Stored Liquid Pig Manure
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it