Net energy content of dry extruded-expelled soybean meal fed to growing pigs using indirect calorimetry
Bibliographic record
Abstract
Feed is the single most expensive input in commercial pork production and at least 50% of this cost can be attributed in supplying energy to the animal thus making energy financially the most vital component. Swine diets can be formulated on a variety of energy systems such as the digestible energy (DE), the metabolizable energy (ME) and the net energy (NE) systems of which the NE system provides more accurate estimates of the energy available to the animal. Energy values of protein-rich feeds are often overestimated when expressed on a DE or ME system (Noblet et al. , 1994). These discrepancies in measurement of available dietary energy have a drastic effect on the economics of pig production and there is, therefore, an ongoing interest in adopting the NE system. The most commonly used protein source in livestock diets is soybean meal (SBM), but it also contains certain antinutritional factors which depress animal growth performance. Studies show that such antinutritional factors are reduced significantly during meal processing (Perilla et al. , 1997). One such process is the combination of extrusion with expelling which produces a SBM product called dry extruded-expelled SBM (DESBM). However, published data pertaining to the energy values of DESBM for grower pigs are limited. The aim of this study was to determine the NE content of DESBM in growing pigs using either an indirect calorimetry (IC) or published prediction equations. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".