Modeling the fate of dietary phosphorus in the digestive tract of growing pigs1
Bibliographic record
Abstract
Environmental effects of excess P from manure and the soaring price of phosphates are major issues in pig production. To optimize P utilization, it is crucial to improve our capacity to predict the amount of P absorbed, while taking into account the main factors of variation. Mathematical modeling can represent the complexity of the processes and interactions in determining the digestive utilization of P in growing pigs. This paper describes and evaluates a model developed to simulate the fate of the dietary forms of P in the digestive tract of growing pigs, with particular emphasis on the effect of dietary Ca and exogenous phytase on P digestive utilization. The model consists of 3 compartments associated with specific anatomical sections: stomach, proximal small intestine, and distal small intestine. The main metabolic processes occurring in these sections are, respectively, P solubilization/insolubilization and phytate P hydrolysis, and P absorption and P insolubilization. Model parameters governing these flows were derived from in vitro and in vivo literature data. The sensitivity analysis revealed that the model was stable within a large range of model parameter values (±1.5 SD). The model was able to predict the efficacy of Aspergillus niger microbial phytase in accordance with literature values, as well as the decreased efficacy of plant phytase compared with microbial phytase. The prediction capabilities of the model were assessed by comparing actual and simulated P and Ca apparent total-tract digestibility (ATTD) based on published pig data not used for model development. Prediction of P digestibility across 66 experiments and 281 observations was adequate [P ATTD observed = 0.24 (SE, 0.943) + 0.98 (SE, 0.0196) × P ATTD predicted; R(2), 0.90; disturbance error (ED), 96.5%], whereas prediction of Ca digestibility across 47 experiments and 193 observations was less accurate (Ca ATTD observed = 11.1 + 0.75 × Ca ATTD predicted; R(2), 0.78; ED, 20.4%). A lack of agreement between experimental and simulated Ca digestibility was found. This model is, therefore, useful in evaluating P digestibility for different feedstuffs and feeding strategies. It can also be used to provide insight for improving dietary P utilization, especially from plant sources, by quantifying the effect of the mean sources of variation affecting P utilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".