106 Swine Modeling: An Integrated Approach to Providing Complete Nutritional Solutions.
Bibliographic record
Abstract
Swine growth models, which predict the responses of pigs to nutrient inputs, have evolved considerably since Whittemore and Fawcett (1976) and Emmans (1981) published the first conceptual frameworks. A proposed integrated model that encompasses three components including a stochastic animal growth model, least cost formulation and an optimization algorithm has been developed and applied in commercial practice. The animal model introduces genetic variation to facilitate the prediction of individual animals and is essential for accurate nutritional optimization as well as for shipping management. The animal growth model is based on the theory that individual animals desire to eat and grow to their genetic potential but are constrained by physical capacity, dietary inadequacies, and environmental limitations, which inhibit the realization of this potential. Simulating individual animals within a population provides the opportunity to integrate the ability of an individual animal to cope with social stressors as well as the interaction between genetics, environment and health status to accurately predict their potential and actual feed intakes and growth rates. The optimization process is based on a genetic algorithm that combines the following: 1) ingredients and diet costs; 2) animal responses particularly feed intake; 3) variation in responses between individual animals; 4) variable and fixed production costs; and 5) a defined revenue generating process (e.g. grading grid). The proposed integrated model incorporates a wide spectrum of nutritional and management processes that empower pork producers to make meaningful production decisions. This presentation will focus on the integration of two key components: health and social stressors, within the biological framework and provide examples of commercial applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".