Application of adaptive neuro-fuzzy inference systems to estimate digestible critical amino acid requirements in young broiler chicks
Bibliographic record
Abstract
This study aimed to find the digestible lysine (d.Lys), digestible sulfur amino acids (d.SAA), and digestible threonine (d.Thr) requirements to optimize body weight gain (BWG) and feed conversion ratio (FCR) via adaptive neuro-fuzzy inference systems (ANFIS) using either the Genetic algorithm (ANFIS-GA) or Particle Swarm Optimization algorithm (ANFIS-PSO) in Cobb-500 chicks from 1 to 10 d of age. The range of amino acids was 90 to 115% of the recommendations for male Cobb-500 chicks. The estimated dietary d.Lys, d.SAA, and d.Thr requirements by ANFIS-GA and ANFIS-PSO to optimize BWG were the same and were 12.10, 8.98, and 7.89 g/kg, respectively. The optimum BWG predicted by ANFIS-GA and ANFIS-PSO were 270 and 266 g, respectively for the 1 to 10 d period. The estimated dietary requirements of d.Lys, d.SAA, and d.Thr to minimize FCR at 0.995 by ANFIS-GA were 12.10, 8.98, and 7.89 g/kg, respectively. Although the estimated d.Lys and d.SAA requirements by ANFIS-PSO and ANFIS-GA were identical, the predicted d.Thr requirement by ANFIS-PSO was 0.01 g/kg higher than by ANFIS-GA to minimize FCR at 0.963. Comparison of goodness of fit in term of root mean square error revealed that the ANFIS-GA prediction was more accurate than ANFIS-PSO. This study demonstrates that the hybrid methodology of ANFIS-GA is as an effective and accurate approach to modeling and optimizing nutrient requirements.
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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 | 0.000 | 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".