From hypothesis to practice, use of a log-linear model to predict and evaluate the response of non starch polysaccharide enzymes in poultry feeds
Bibliographic record
Abstract
Enzymes have been widely used as a feed additive to improve growth performance of poultry and domestic animal. However, it has not been possible to accurately predict and evaluate the response obtained with a given dose of a specific enzyme preparation. The objective of this research was to determine if a new mathematical approach, a log-linear prediction model equation, could be used to predict and evaluate the response of chicks to a dietary enzyme supplementation. Two dose-response experiments with Leghorn chicks and those from several publications were studied to determine whether a simple general equation could be used to predict the relationship between the amount of a feed enzyme added to a diet and chick performance. An in vitro dietary viscosity assay was developed to determine whether it could be used in conjunction with the model as the predictor or evaluator. The results demonstrated that the model was able to accurately predict (high r2 values) the response of chicks fed diets containing the different amounts of an enzyme and different proportions of two cereals. The slope of the model was a measure of the efficacy of the feed enzyme. The efficacy, in turn, was able to correctly evaluate the effects of different feed enzymes when added to a diet and to identify the target cereal for an enzyme. In addition, a Multi-purpose Enzyme Analyzer has been developed based on the model. The analyzer was able to determine the optimal amount of an enzyme and a substituted cereal that should be used in a diet for maximal profit, and to determine the amounts and the expected prices of the enzyme and cereal that will yield a given profit. Therefore, the effect of a feed enzyme could be evaluated using maximal profit as a criterion. Thus, the most profitable effect of different feed enzymes and the cerea s that should be used for a given feed enzyme could be determined. Furthermore, a dietary viscosity assay has been developed. The results indicated that there was a linear relationship between the log of dietary viscosity change measured by the assay and the log of amount of enzyme added to a diet (r2 = 0.99, P < 0.005). The values from the assay were able to predict the response of chicks to a feed enzyme and also evaluate the efficacy of different feed enzymes, especially for those enzymes that hydrolyzed the viscous compounds in the diet. These studies demonstrated that the response of chicks to a feed enzyme and the efficacy of the enzyme could be predicted and evaluated on the basis of a log-linear model using different criteria (performance and economic return), and different type of studies (in vivo and in vitro).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".