33 Award Talk: Precision calcium and phosphorus nutrition in pigs and poultry.
Bibliographic record
Abstract
Abstract Monogastric livestock use little plant phosphorus (P), that is in the form of phytate (PP) because they do not have the phytase enzyme. In high-production, high-density countries, this leads to excessive fertilization that leaches into waterways and can lead to eutrophication of water bodies when animals excrete more P into the environment than plants need. In addition, to meet their P needs, mineral phosphate additions, a non-renewable resource that was recently estimated to peak around 2030 and replenish 100 years later and is also essential for crops, are required. Addressing this threat of a P resource shortage for the food system needs precise P nutrition which rely on research to understand modulating factors and precisely assess feed ingredients P value and pig and poultry requirements. Among the important factors to control, there is calcium that is essential to fixed P into bone but can reduced P absorption by the formation of insoluble complexes in the intestine. It is also interesting to note that birds may present a Ca apatite which is less true in swine. Also, limestone the main source of calcium is a important element to understand better as its solubility and particle size drive its interaction with P in the gut especially in poultry. Supplementation of diets with exogenous phytase is a common practice that allow increasing the use of PP. Continuous advances in protein engineering led to development of new generations of phytases and the 4th generation is arriving on the market offering the possibility to reduce or even eliminate the use of phosphates if its use is optimize. Optimizing the use of P also rely in multicriteria approach as P is crucial for bone, growth performance while it is useful for crop production, but when in excess can cause eutrophication. To reach the goal of optimizing P utilization, a combination is proposed. First experiments to measured growth performance, feces and urine excretion, plasma concentration and the most important parameters bone mineralization. It’s nowadays possible to follow the body composition including bone mineral content with dual X-ray absorptiometry, a powerful tool for research in micromineral. Then meta-analyses can be used to generate data for empirical (e.g., prediction equation, parameter estimation) and mechanistic model development and their validation. This allows us to develop robust multi-criteria models that adapt to context and can even perform real-time simulations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.122 | 0.039 |
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".