Development of a Multipurpose Feed Enzyme Analyzer to Estimate and Evaluate the Profitability of Using Feed Enzyme Preparations for Poultry
Bibliographic record
Abstract
Previous studies have demonstrated that a log-linear equation could accurately predict chick performance when a feed enzyme was added to a diet and that the slope of the equation provided a measure of the efficacy of different enzymes. The objective of the study was to develop a software package, a Multipurpose Feed Enzyme Analyzer (MPFEA), based on an equation designed to evaluate the profitability of using feed enzymes. A high correlation between the efficacy of different feed enzymes (B values, the slopes of the equations) and the maximal profits was obtained when feed enzymes were added to a barley-based diet (r2 = 0.99, P < 0.0005). In contrast, there was a low correlation between the B values and the maximal profits when a feed enzyme was added to different cereal-based diets (r2 = 0.61, P = 0.2171). It appeared that there is not always a close association between efficacy of an enzyme when added to different cereal-based diets and the corresponding profitability. The MPFEA was highly versatile, as any combination of inputs such as the amounts of a feed enzyme and a substituted cereal required to yield a profit level could be determined. In conclusion, the MPFEA can accurately evaluate profitability of using different feed enzymes; select the most profitable cereal for a given feed enzyme; determine the optimal amounts of a feed enzyme, a cereal, or both; and even estimate the alternate price for a feed enzyme and a cereal. It should provide a useful tool for nutritionists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".