Application of pig growth models in commercial pork production
Bibliographic record
Abstract
Pig growth models can be useful tools for identifying optimum management strategies for individual grower-finisher pig units, by integrating knowledge of nutrient utilization for growth and animal-environment interactions into one system. In addition, these models can be used to demonstrate basic principles of nutrient utilization for growth in the pig, to examine “what-if” scenarios, to aid in the development of pig breeding programs and to develop effective research programs. Models used in commercial pork production should represent the biology of growth in the pig and should be flexible, so that they can be focused easily on the needs and special conditions pertaining to particular growing–finishing pig units. For proper application of pig growth models in practice, pig units should be characterized reasonably accurately. This applies in particular to the upper limit to body protein deposition that pigs can achieve under practical conditions, feed intake at various stages of growth and the alternative feeding strategies that can be considered. Some illustrative examples of the commercial application of a pig growth model under Canadian conditions are provided. Key words: Pig, growth, models, application
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".