Modeling the metabolic fate of dietary phosphorus and calcium and the dynamics of body ash content in growing pigs
Bibliographic record
Abstract
A better understanding of the fate of dietary P use by growing pigs will allow an optimization of P use and enhance sustainable practices. The optimization of P utilization is complicated by the multiple criteria, such as growth performance, bone mineralization, and manure P used for assessment of needs. Mathematical modeling is a useful tool to describe relevant biological mechanisms and predict relationships that describe the whole system behavior. Modeling allows development of robust multicriteria approaches to optimize P utilization, feeding cost, and manure application cost. This paper describes and evaluates a model developed to simulate the fate of dietary P, that is, to simulate its digestive and metabolic utilization through digestion, soft tissue, and ash modules. The digestion module takes into account the varied sources of dietary minerals including responses to microbial and plant phytase and Ca and P interactions and predicts absorption and fecal excretion. The soft tissue module simulates the growth of the protein and is based on InraPorc model principles. The ash module simulates the partitioning of absorbed Ca and P into the bone, protein, and lipid compartments as well as urinary excretion. Model behavior showed that the model was able to accurately represent the impact of Lys deficiency on P retention, of Ca and P imbalances, and of Ca and P depletion and repletion sequences. The model’s prediction capabilities in simulating whole-body protein, Ca, P, and ash based on published data showed high accuracy, with a slope and intercept that did not differ from 1 and 0, respectively, and an error due to disturbance (ED; variance not accounted for by regression of observed on predicted values). The model’s prediction capabilities in simulating balance trial data showed good accuracy for apparent total tract digestibility (ATTD) of P (observed = –0.77 + 1.06 predicted) and P retention coefficient (observed = –4.5 + 1.15 predicted) with an ED of 89% for both criteria. The model’s prediction capabilities in simulating Ca ATTD and Ca retention coefficient are lower (ED of 88 and 28%, respectively). This model simulates body ash independently of body protein and accounts for the impact of past and current dietary Ca and P supply. That ability is essential for the real-time adaptation of mineral supplies to suit individual production objectives, which would contribute to the overall success of pig production.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".