Development and evaluation of a dynamic model of calcium and phosphorus flows in layers
Bibliographic record
Abstract
Phosphorus is an essential nutrient involved in most metabolic processes. Most of the interest in Ca metabolism relates to eggshell formation. Although the eggshell is composed of Ca carbonate, metabolism of both Ca and P is closely related such that a deficiency in one can interfere with proper utilization of the other. To understand Ca and P metabolism properly, modeling can be of paramount importance. A new dynamic and mechanistic model of P and Ca metabolism in layers has been developed to simulate diurnal changes in Ca and P and the hourly requirement of the layer for those minerals. The model consists of 8 state variables representing Ca and P pools in the crop, stomachs, plasma, and bone. The flow equations are described by Michaelis-Menten or mass action forms. An experiment that measured Ca and P uptake in layers fed different Ca concentrations during shell-forming days was used for model evaluation. The experiment showed that Ca retained in body and egg decreased from 62.5 to 50.5% of Ca intake when the Ca in diet was increased from 25 to 45 mg/g of feed. The model simulations were in agreement with the trend. Predictions of Ca retention in bone and egg were 63.2, 56.1, and 55.3% for low, medium, and high dietary Ca concentrations. The experimental results showed that P retention in body and egg increased significantly from 11.5% of absorbable P intake at the lowest Ca inclusion concentration to 24.1% at the highest. The model also predicted an increase in P retention in bone and egg from 8.4 to 25.4% of absorbable P intake at the lowest and highest concentration of Ca inclusion, respectively. The advantage of the model is that absorption and utilization can be monitored on an hourly basis and that adjustments can be made accordingly. The model successfully showed how the availability of one mineral affects the utilization of the other and is a useful tool to evaluate feeding strategies aimed at reducing P excretion to the environment in poultry manure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".