Effect of micronization of peas and enzyme supplementation on nutrient excretion and manure volume in growing pigs
Bibliographic record
Abstract
The effect of micronization of peas and enzyme supplementation on performance, nitrogen and phosphorus excretion as well as manure output was investigated in a study with 12 Cotswold gilts (26.9 ± 3.6 kg initial body weight). Three diets based on barley-raw peas (control), barley-micronized peas, or barley-micronized peas plus a multi-enzyme blend were used. The study was conducted in two blocks and within a block; each diet was randomly assigned to four pigs to give a total of eight observations per diet. Pigs fed micronized peas and micronized peas plus enzyme diets had higher (P < 0.05) feed conversion than the control. Compared with the control, micronization of peas reduced (P < 0.05) wet and dry fecal output by 33 and 25%, respectively. Urine volume was not influenced (P > 0.05) by dietary treatment. Total fecal phosphorus excretion was lower (P < 0.001) in gilts fed the micronized peas or micronized peas plus enzyme diets compared with the control. Micronization of peas reduced (P < 0.05) total nitrogen excretion in pig manure by 21.5% (17.39 vs. 13.65 g d-1). In conclusion, formulating pig grower diets with micronized peas improves nitrogen and phosphorus utilization and reduces total manure volume. Key words: Pigs, nitrogen, phosphorus, manure volume, micronized peas
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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) | 0.000 | 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) | 0.000 | 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".