Characterization of useful energy content in swine and poultry feed ingredients
Bibliographic record
Abstract
For effective use of feed ingredients in diets for the various classes of animals, it is important that the feeding value of feed ingredients is properly estimated. This applies in particular to the useful or bio-available energy content, as feed energy generally represents the single largest cost-factor in animal production. In spite of their limitations, digestible energy (DE) and metabolizable energy (ME) systems are used widely in North America to estimate the useful or bio-available energy content of feeds and feed ingredients for pigs and poultry, largely because experimental procedures to establish DE and ME values are relatively simple. Some of the limitations of DE and ME systems can be overcome by using empirical net energy (NE) systems, whereby feed or feed ingredient NE content is predicted from digestible nutrient contents. However, empirical NE systems require estimates of the animal’s maintenance NE needs, which cannot be measured directly and have been estimated to vary between 489 and 750 kJ kg-1 BW0.60. Moreover, estimated feed or feed ingredient NE contents only apply to one particular animal state. The practical application of NE prediction equations requires an accurate characterization of nutrient contents and digestibility of feeds and feed ingredients. An accurate and flexible assessment of animal and feed effects on energy utilization requires the use of mathematical models in which transformations and use of dietary nutrients for different body functions are represented. Effective use of such nutrient flow models requires accurate characterization of feeds and feed ingredients and of animals in aspects of nutrient partitioning for the various body functions. This type of model can be used to predict accurately the useful energy supply from feeds and feed ingredients for specific animal states for diet formulation purposes. Nutrient utilization models may be refined to explore additional aspects of nutrient utilization, such as dynamics of nutrient absorption, the utilization of nutrients via alternative and competing metabolic pathways and inter-organ nutrient metabolism. Key words: Digestible energy, energetics, feed ingredients, metabolizable energy, net energy, nutrition, pigs, poultry
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".