Predicting caloric and feed efficiency in turkeys using the group method of data handling-type neural networks
Bibliographic record
Abstract
Neural networks (NN) are a relatively new option to model growth in animal production systems. One self-organizing submodel of artificial NN is the group method of data handling (GMDH)-type NN. The use of such self-organizing networks has led to successful application of the GMDH algorithm over a broad range of areas in engineering, science, and economics. The present study aimed to apply the GMDH-type NN to predict caloric efficiency (CE, g of gain/kcal of caloric intake) and feed efficiency (FE, kg of gain/kg of feed intake) in tom and hen turkeys fed diets containing different energy and amino acid levels. Involved effective input parameters in prediction of CE and FE were age, dietary ME, CP, Met, and Lys. Quantitative examination of the goodness of fit for the predictive models was made using R2 and error measurement indices commonly used to evaluate forecasting models. Statistical performance of the developed GMDH-type NN models revealed close agreement between observed and predicted values of CE and FE. In conclusion, using such powerful models can enhance our ability to predict economic traits, make precise prediction of nutrition requirements, and achieve optimal performance in poultry production.
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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 | 0.001 | 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".