Digestible phosphorus requirement of grower pigs
Bibliographic record
Abstract
Phosphorus excretion may have a major impact on the environment if it is not managed properly. The success of management strategies for reducing P excretion of pigs is dependent partly on more accurate estimates of P requirements, specifically digestible instead of total P requirements. Performance and metabolism studies were conducted to determine digestible P requirements of grower pigs based on performance, plasma and bone P, and P excretion and retention variables, using 200 pigs (23 ± 0.9 kg) and 20 barrows (54 ± 3.1 kg), respectively. Pigs were fed one of five concentrations of dietary digestible P (0.19, 0.24, 0.33, 0.35, and 0.38%). Increasing digestible P quadratically increased average daily gain (ADG) (P < 0.01), feed intake (P < 0.05), and feed efficiency (P < 0.001). Barrows had a higher ADG than gilts (0.890 vs. 0.838 kg d-1; P < 0.05); however, digestible P requirement was higher for gilts than for barrows (6.92 vs. 6.17 g d-1 or 0.36 vs. 0.32% in diet; P < 0.05). In barrows, increasing digestible P intake quadratically increased P in plasma and urine (P < 0.01), and linearly increased P in faeces (P < 0.01), suggesting that P excretion depends on excess P intake. Using regression analysis, digestible P requirements were 6.45 g d-1 with ADG, 7.46 g d-1 with bone P, 6.01 g d-1 with plasma P, 3.61 g d-1 with urinary P, 5.86 g d-1 with retained P, and 5.11 g d-1 with retained N. Feeding P closer to pig requirements will reduce P excretion. Key words: Phosphorus, requirement, pig
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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) | 0.000 | 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) | 0.001 | 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".