Modeling the efficiency of phosphorus utilization in growing pigs1
Bibliographic record
Abstract
Microbial phytase has been used to reduce P excretion from swine to mitigate environmental pollution. The objective of the study was to quantify the effect of feeding a low-P phytase-supplemented diet on growth and P utilization in growing pigs using mathematical models. A total of 20 weaned piglets (BW = 6.5 kg) housed in metabolism cages were randomly assigned to a standard diet (STD) or P-amended diet containing reduced P content and supplemented with phytase (AMN) with 10 pigs/diet. Body weight and feed consumption were recorded weekly so complete growth and cumulative P intake (cPI) curves could be modeled. A function with fixed point of inflexion (Gompertz) and a variable point of inflexion (generalized Michaelis-Menten) were considered in determining bioequivalence by analyzing BW vs. age relationships, whereas the monomolecular function was used to describe BW vs. cPI. All functions were incorporated into a nonlinear mixed effects model, and a first-order autoregressive correlation structure was implemented to take into account repeated measures. There was no difference between the 2 groups in final BW when the Gompertz equation was fitted (176 vs. 178 kg with SE of 7 kg for the STD and AMN, respectively) or the rate parameter (0.0140 vs. 0.0139 with SE of 0.0004 for the STD and AMN, respectively). The generalized Michaelis-Menten equation also showed a similar trend. When BW was expressed as a function of cPI the derivative with respect to cPI represented P efficiency, so it was possible to analyze the expected difference of the 2 diets in using P for BW gain and express it as a continuous function of cPI. The analysis showed through the entire growth period the difference in P efficiency was different from zero. On average, 56 g of supplemented inorganic P was consumed by a pig fed the AMN to reach market weight. In contrast, 309 g of supplemented inorganic P was consumed by the group fed the STD to reach similar BW. It would depend on other factors, but feeding pigs the AMN can result in economic benefit. Pigs fed the AMN excreted 19% less P compared with those fed the STD. In conclusion, nonlinear mixed model analysis (with repeated measures) was suitable for growth and efficiency analysis and showed that pigs fed the AMN consumed less than 20% of the inorganic P and performed as well as those fed the traditional inorganic P supplemented diet. The implications for mitigating P pollution, especially in areas where P loading is already problematic, are substantial.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".