Assessment of the Nitrogen Correction Factor in Evaluating Metabolizable Energy of Corn and Soybean Meal in Diets for Broilers
Bibliographic record
Abstract
Three experiments were carried out to determine AME and AMEn of corn and soybean meal (SMB) in diets for growing broilers. In experiments 1 and 2, ingredient-specific basal diets or a combination of these basal diets with corn and SBM were prepared. For corn, the substitution was 25, 50, or 75% of the total diet, whereas SBM substitution was at 10, 20, or 30%. In experiment 1, birds were fed the experimental diets continuously from 0 to 33 d, and AME and AMEn were determined during 9 to 12 d and 30 to 33 d of age. In experiment 2, birds were fed the experimental diets only around the time of the collection period. The AMEn of corn was 95 to 97% of corresponding AME, whereas for SBM, AMEn was 93 to 88% of AME. Linear regression was used as an alternative method of calculating ingredient energy values resulting in a significant regression of diet AME and AMEn content on inclusion level, for each period of time and for each ingredient (corn and SBM). Based on varying inclusion levels of test ingredients in the diet, the extrapolated AME and AMEn of corn were estimated more precisely (R2 = 0.90 to 0.95) than those of SBM (R2 = 0.57 to 0.85), suggesting that the variability of AME and AMEn is better explained by a linear regression of AME or AMEn on percentage of inclusion. For corn, AME and AMEn were little affected by age, and the effect of N correction was consistent at around 3%. Determined energy values of SBM were more variable. Experiment 3 was conducted to assess the effect of formulating diets based on either AME or AMEn on broiler performance. A 2-sample t-test was implemented examining AME vs. AMEn formulation. The analyses for numerous production and carcass traits were nonsignificant except for the case of less abdominal fat in birds fed diets formulated to AME rather than AMEn (P < 0.01). These results showed that the use of the N correction imposed a penalty to corn of 3 to 5% and SBM of 7 to 12%.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".