Neural network models for predicting early egg weight in broiler breeder hens
Bibliographic record
Abstract
In this study, neural network (NN) models were developed to predict egg weight in broiler breeder hens. The input variables for developing the NN models were ME (kcal/bird per day) and CP, TSAA, Lys, Ca, available P, and linoleic acid (all as g/bird per day). By grouping the data collected from 98 breeder houses into weekly intervals, 4 NN-based models were developed for 25 to 28 wk of age. From the available data set (98 data lines for each week), a training set (n = 69) and a testing set (n = 34) were extracted. The models developed were subjected to an optimization algorithm to find the optimal values of input variables that might maximize early egg weight in broiler breeder hens. According to goodness-of-fit statistical criteria, the NN-based models could effectively estimate egg weight in broiler breeder hens. Maximum egg weight, using optimization results, may be obtained with 406, 454, 466, and 487 kcal/bird per day of ME; 21.3, 24.9, 25.6, and 26 g/bird per day of CP; 0.88, 0.97, 1.09, and 1.1 g/bird per day of TSAA; 1.02, 1.1, 1.22, and 1.23 g/bird per day of Lys; 4.13, 4.8, 5.2, and 5.27 g/bird per day of Ca; 0.52, 0.57, 0.6, and 0.62 g/bird per day of available P; and 1.97, 2.01, 2.28, and 2.3 g/bird per day of linoleic acid for 25, 26, 27, and 28 wk of age, respectively. Therefore, the energy and other nutrient requirements of broiler breeder hens for maximum egg weight do not change in parallel with age. Moreover, the Ross guideline recommendation seemed to underestimate the nutrient requirements of hens during these weeks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".