Evaluation of broiler chicks responses to protein, methionine and tryptophan using neural network models
Bibliographic record
Abstract
In the present study, neural networks (NN) were developed to investigate the responses (average daily gain [ADG] and feed efficiency [FE]) of broiler chicks to protein and two amino acids (AA), namely methionine (Met) and tryptophan (Trp).Separate NN models were constructed for responses to protein and Met and protein and Trp.Comparisons between NN and response surface models revealed higher accuracy of prediction with the NN models.The relative importance of the input variables (protein and AA) on model output (ADG and FE) was assessed using a sensitivity analysis technique.Results indicated that dietary protein is a more important variable than AA (Met and Trp).Optimal values of the input variables (protein and AA) required to maximize ADG and FE in were obtained by subjecting all constructed NN to an optimization algorithm.The optimization algorithm for the protein and Met response models revealed that diets containing 216 g/kg of protein and 5.45 g/kg of Met lead to maximum ADG, whereas maximum FE is achieved with diets containing 222.6 and 5.85 g/kg of protein and Met, respectively.The optimization algorithm for protein and Trp responses showed that 234 g/kg of protein and 2.6 g/kg of Trp in the diet lead to maximum ADG, while maximum FE is achieved with diets containing 240 g/kg of protein and 3.1 g/kg Trp.The optimization results therefore suggest that protein and AA requirements for maximum FE are higher than for maximum ADG.
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 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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".