Review: Prediction of variation in energetic value of wheat for poultry
Bibliographic record
Abstract
Yegani, M. and Korver, D. R. 2012. Review: Prediction of variation in energetic value of wheat for poultry. Can. J. Anim. Sci. 92: 261–273. Variations in physical and chemical characteristics of wheat can significantly influence the energy availability of this feed ingredient for poultry. These variations can result in inefficiencies in the form of over- or under-formulation of the diets at commercial feed mills or on poultry farms. Therefore, having a clear understanding of the variations is of paramount importance in the formulation of poultry diets as they can have negative consequences for production performance of birds. There are a large number of factors that can contribute to variations in energy availability of wheat for poultry. This review is intended to briefly discuss these factors and also practical approaches that can be used to predict these variations. These approaches include measuring physico-chemical characteristics, in vivo digestibility trials, in vitro digestibility techniques, and near infrared reflectance spectroscopy (NIRS). There are limitations associated with physico-chemical and in vivo measurements. However, in vitro digestibility techniques are simple and fast and can provide data for database development and ongoing calibrations of NIRS systems. Near infrared reflectance spectroscopy has enormous potential to predict variations in wheat apparent metabolizable energy, leading to more accurate diet formulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".